Research output
Statistical methods, astronomical survey analyses, and research led by students and postdocs in our group.
77 of 143 papers belong to one of these four primary authorship categories in my curated ADS libraries; the remaining 66 are broader collaboration papers included in the total and grouped as “Other collab.” in the authorship breakdown below.
Reading these summaries: The research mix bar weights each paper by its topic probabilities across our four core research areas. The authorship breakdowns separate papers I led directly from those led by postdoctoral fellows and students in our group, papers where I made a major methodological or analysis contribution, and larger survey collaborations. RIQ (Research Impact Quotient), defined as √(total citations) divided by career years active, normalizes citation growth by career length; the shaded band shows the 60–150 range typical of established astronomers (Pepe & Kurtz 2012).
Generative artificial intelligence (AI) excels at producing complex data structures (text, images, videos) by learning patterns from training examples. Across scientific disciplines, researchers are now applying generative models to 'inverse problems' to directly predict hidden parameters from observed data along with measures of…
We present a deep, high-angular-resolution 3D dust map of the southern Galactic plane over 239° < l < 6° and ∣ b∣ < 10° built on photometry from the DECaPS2 survey, in combination with photometry from VISTA Variables in the Via Lactea, the Two Micron All Sky Survey, and "Unofficial" Wide-field Infrared Survey Explorer and…
Gyrochronology is a technique for constraining stellar ages using rotation periods, which change over a star’s main-sequence (MS) lifetime due to magnetic braking. This technique shows promise for MS FGKM stars, where other methods are imprecise. However, the observed dispersion in rotation rates for similar coeval stars has historically…
Generative artificial intelligence (AI) excels at producing complex data structures (text, images, videos) by learning patterns from training examples. Across scientific disciplines, researchers are now applying generative models to 'inverse problems' to directly predict hidden parameters from observed data along with measures of uncertainty. While these predictive or posterior-based methods can handle intractable likelihoods and large-scale studies, they can also produce biased or overconfident conclusions even without model misspecifications. We present a solution with Frequentist─Bayes (FreB), a mathematically rigorous protocol that reshapes AI-generated posterior probability distributions into (locally valid) confidence regions that consistently include true parameters with the expected probability, while achieving minimum size when training and target data align. We demonstrate FreB's effectiveness by tackling diverse case studies in the physical sciences: identifying unknown sources under dataset shift, reconciling competing theoretical models, and mitigating selection bias and systematics in observational studies. By providing validity guarantees with interpretable diagnostics, FreB enables trustworthy scientific inference across fields where direct likelihood evaluation remains impossible or prohibitively expensive.
Observations of the early Universe (z ≳ 4) with JWST reveal galaxy populations with a wide range of intrinsic luminosities and colors. Bursty star formation histories (SFHs), characterized by short-term fluctuations in the star formation rate (SFR), may explain this diversity, but constraining burst timescales and amplitudes in individual galaxies is challenging given degeneracies and sensitivity limits. We introduce a population-level simulation-based inference framework that recovers the power spectrum of SFR fluctuations by forward modeling galaxy populations and distributions of rest-frame UV to rest-frame optical spectral features sensitive to such timescales. We adopt a stochastic SFH model based on a power spectral density (PSD) formalism spanning 1 Myr–10 Gyr. Using simulated samples of N = 500 galaxies at z ∼ 4 with typical JWST/NIRSpec uncertainties, we demonstrate that (i) the PSD can be …
New observations from the Gaia spacecraft have traced an emerging demographic of low-mass associations disconnected from larger associations or giant molecular clouds. The first of these associations were recently characterized, but the star-forming environments they trace remain unknown. Using new velocities and ages alongside literature catalogs, we uncover the origins of 16 low-mass associations (M ≲ 100 M⊙, τ ≲ 50 Myr) using dynamical trace back. We reveal that three groups of currently disparate populations share common formation sites, comprising the Leo, Canis Major North─Monoceros South (CaNMoS), and Aquila East─Northern Scutum Associations. Twelve of 16 associations have plausible connections to larger complexes, six of which formed while moving outward from well-established multigenerational star-forming events that drive known or suspected bubbles. We find that feedback from the oldest cospatial and comoving relatives of these associations can explain the current morphologies of the Local and Orion─Eridanus Bubbles, along with the formation of related associations like Sco─Cen and Orion OB1. Most remaining populations show evidence for triggered star formation. In the Leo Association, high vertical velocities and a deceleration signature suggest that it formed out of an intermediate-velocity cloud colliding with gas in Orion, which would make it the first known case of star formation in one of these clouds. The other newly defined associations show similar asymmetric velocity signatures, such as CaNMoS, which may trace bubble-driven acceleration or a cloud collision. We conclude that the lowest-mass young associations remain undiscovered, and that these populations may have a critical role revealing the small gas overdensities that trace the processes sculpting Galactic star formation.
The spatial extent of the environment's impact on galaxies marks a transitional region between cluster and field galaxies. We present a data-driven method to identify this region in galaxy clusters with masses at z=0. Using resolved galaxy samples from the largest simulation volume of IllustrisTNG (TNG300-1), we examine how galaxy properties vary as a function of distance to the closest cluster. We train neural networks to classify galaxies into cluster and field galaxies based on their intrinsic properties. Using this classifier, we present the first quantitative and probabilistic map of the transition region. It is represented as a broad and intrinsically scattered region near cluster outskirts, rather than a sharp physical boundary. This is the physical detection of a mixed population. In order to determine transition regions of different physical processes by training property-specific models, we categorise galaxy properties based on their underlying physics, i.e. gas, stellar, and dynamical. Changes to the dynamical properties dominate the innermost regions of the clusters of all masses. Stellar properties and gas properties, on the other hand, exhibit transitions at similar locations for low mass clusters, yet gas properties have transitions in the outermost regions for high mass clusters. These results have implications for cluster environmental studies in both simulations and observations, particularly in refining the definition of cluster boundaries while considering environmental preprocessing and how galaxies evolve under the effect of the cluster environment.
Large-scale spectroscopic surveys have collectively observed millions of stars across the Milky Way, but each derives stellar labels using independent pipelines with distinct modeling assumptions, introducing systematic offsets that obscure signals in chemical space and hinder large-scale Galactic archeology. We present a unified deep-learning framework that delivers atmospheric parameters, chemical abundances for 20 elements, distances, and ages—all on a single, self-consistent scale—for an arbitrary number of spectroscopic surveys simultaneously. Our approach uses a Transformer model that ingests spectra of an arbitrary wavelength range and resolution, trained end to end as a single model across all surveys, eliminating the need for posthoc recalibration. We apply this framework to spectra from APOGEE DR17, GALAH DR3, DESI DR1, and Gaia Radial Velocity Spectrometer DR3, spanning resolutions from R ∼ 2000 to 28,000 and wavelengths from the optical to the near-infrared. On high-resolution APOGEE spectra, the model achieves precisions of 18 K in Teff, 0.04 dex in <inline-formula> <mml:math><mml:mi>log</mml:mi><mml:mspace></mml:mspace><mml:mi>g</mml:mi></mml:math> </inline-formula>, 0.015 dex in [Fe/H], and <0.03 dex across all abundances; on lower-resolution DESI spectra, typical precisions are 51 K, 0.09 dex, 0.04 dex, and ∼0.06 dex, respectively. Cross-survey comparisons demonstrate that labels for the same stars observed by different surveys are consistent within model uncertainties; we further validate against external distance catalogs and open cluster metallicities and ages. The resulting homogeneous catalog enables Galactic archeology at an unprecedented scale and consistency, and the framework is readily extensible to forthcoming spectroscopic surveys such as Sloan Digital Sky Survey-V, WEAVE, and 4MOST. The living catalog is publicly available at https://doi.org/10.5281/zenodo.19830515.
The chemical abundances of a stellar population encode information about nucleosynthesis and its astrophysical sites, but this information is confounded by the specific star formation history of the host galaxy. As a result, placing empirical constraints on supernova yields and timing using abundances has been very challenging. We introduce a galactic chemical evolution model DLEIY that uses an observed star formation history and metallicity distribution to reduce these confounding factors. Using a joint statistical model of the dwarf spheroidal galaxies Sculptor and Fornax, simultaneous constraints on population-averaged yields and galactic outflows are achieved with DLEIY, without fixing the absolute scale of nucleosynthetic yields. The Fe yield from core-collapse supernovae is consistent with existing theoretical yield models, while the measured Mg yield is a factor of 2–4 higher, corroborating previous …
Zeeman observations provide the only direct probe of line-of-sight (LOS) magnetic fields in the interstellar medium. To evaluate their accuracy and limitations, we generate synthetic H I Zeeman spectra from magnetohydrodynamic simulations and idealized cloud models, and analyze the resulting Stokes I and V profiles using two complementary methods. Approach I uses the classical relation between Stokes V and dI/dν to estimate LOS-averaged magnetic fields, achieving an upper-limit relative error of ∼16% (half-width of 68.27% confidence interval) for a representative noise level of 0.014 K. Approach II applies Gaussian decomposition to Stokes I and V to estimate component-level magnetic fields, yielding a ∼13% relative error quantifying the same confidence range, reflecting the intrinsic uncertainty of such Zeeman estimates. Both approaches recover the original fields under uniform-field conditions and remain robust in turbulent environments. Approach I provides a simple and reliable LOS-averaged field estimate, while Approach II, although more complex, offers statistical insight into magnetic field variations along the LOS. We further show that joint fitting of Stokes I and V generally outperforms sequential fitting, particularly in the presence of attenuation. Increasing noise eightfold produces a more modest rise in uncertainty, doubling to a ∼26% relative error, while substantial optical depth introduces only a minor additional contribution to the overall uncertainty. Applying these methods to FAST observations of the L1544 star-forming region, we confirm the previously reported LOS magnetic field strength, demonstrating the validity of Zeeman analysis in this benchmark core.
Galaxy structure is wavelength-dependent, as different photometric bands trace distinct stellar populations and structural components. We present per-galaxy Bayesian posterior distributions of the bulge-to-total light ratio (L_B/L_T), effective radius (R_e), and flux (F) in the g, r, and i bands for nearly 10 million galaxies across ~1400 deg^2 of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) Wide survey at z <= 0.75 and m < 23. To our knowledge, this is the largest multi-band catalog of bulge-disk structural parameters with per-galaxy posteriors. ~3.9 million of the above galaxies have full multi-band coverage. Measurements are generated using the Galaxy Morphology Posterior Estimation Network (GaMPEN), a Bayesian deep-learning framework. We demonstrate that we can adapt GaMPEN from previously-trained single-band models to the new bands by using transfer learning on only ~0.5% of our dataset. Despite this extremely small training set, GaMPEN successfully reproduces light-profile fitting measurements and its output posteriors are extremely well-calibrated with <= 6% mean deviation. This approach offers a scalable pathway to structural parameter measurements with robust uncertainties for Rubin, Euclid, and Roman data, which we will pursue in forthcoming work. While structural measurements remain robust across most bands and redshift ranges, performance degrades significantly for the g-band at z >= 0.5, where it increasingly probes rest-frame ultraviolet emission; these measurements are therefore excluded from the public release. This public catalog will enable large-scale statistical studies of morphological color gradients, wavelength-dependent galaxy structure, and galaxy evolution.
Estimating galaxy redshifts (a proxy for distance) from imaging data alone, also known as photometric redshifts (photo-z's), is essential for studies of extragalactic astrophysics and cosmology. However, data-driven estimators rely on spectroscopic training datasets, which are scarce and systematically biased toward bright and easily observed galaxies. In this work, we show that tabular foundation models (TFMs), whose synthetic pretraining amounts to a learned prior over regression problems, can outperform bespoke estimators using only in-context learning. Evaluating on a widely used benchmark dataset, we find that TFMs predicting full probability distributions in a single forward pass systematically outperform bespoke estimators across both representative and biased training data regimes, matching or exceeding baselines trained on over 40,000 galaxies with just 1,000 training context examples. We also show that the TFM advantage is largest when little to no calibration data are available, and that it persists even when the bespoke baselines are supplemented with up to 10,000 representative calibration spectra. This shows that TFMs offer a sample-efficient framework for upcoming imaging-only astronomical surveys, where observational selection bias can be mitigated simply by including a small representative calibration sample in context.
Astronomical observations include images, spectra, and light curves, while the scientific literature describes these objects in natural language. We present AstroBridge, a framework that connects these observations to an LLM through a literature-assisted caption generation pipeline. We used catalog metadata to match observations with scientific texts that describe the same objects. A vision-language model generates descriptive captions from representations of observations, matched passages, and available metadata, creating observation-caption pairs without manual captioning. We train AstroBridge to predict captions directly from raw observations. Modality-specific frozen encoders extract representations that a shared, trainable fusion network aligns with the embedding space of an LLM. In caption-based evaluation of spectra, AstroBridge achieves 91.5% accuracy in distinguishing quasars from galaxies, compared to 69.0% from the base model prior to fine-tuning. Limitations remain in galaxy morphology, classification of minority supernova classes, and the factual quality of generated captions. We believe that AstroBridge's framework is a step towards the goal of aligning the totality of astronomy's diverse, exotic data through the lingua franca of text.
Characterizing protostellar outflows is fundamental to understanding star formation feedback, yet traditional methods are often hindered by projection effects and complex morphologies. We present a multimodal deep learning framework that jointly leverages spatial and spectral information from CO observations to infer protostellar mass, inclination, and position angle (PA). Our model, trained on synthetic Atacama Large Millimeter/submillimeter Array (ALMA) observations generated from 3D magnetohydrodynamic simulations, utilizes a cross-attention fusion mechanism to integrate morphological and kinematic features with probabilistic uncertainty estimation. Our results demonstrate that Vision Transformer architectures significantly outperform convolutional networks, showing remarkable robustness to reduced spatial resolution. Interpretability analysis reveals a physically consistent hierarchy: spatial features dominate across all parameters, whereas spectral profiles provide secondary constraints for mass and inclination. Applied to observational ALMA data, the framework delivers stable mass and PA estimates with exceptionally tightly constrained inclination angles. This study establishes multimodal deep learning as a powerful, interpretable tool for overcoming projection biases in high-mass star formation studies.
How galaxies assemble depends on processes ranging from small-scale baryonic physics to large-scale cosmological assembly, which are difficult to disentangle with observations alone. Using ∼63,000 simulated galaxies from seven cosmological simulations spanning hydrodynamical, semi-analytic, and semi-empirical models, we explore how specific star formation histories (sSFHs) vary across modelling assumptions. Using non-linear dimensionality reduction (UMAP), we find that sSFH shape variation organizes along a few smooth axes in a 2D embedding, broadly corresponding to (1) peak timing, (2) peak sharpness, and (3) subsequent quenching rate. We identify sSFH shapes that many simulations share ("common"), well described to first order by dark matter halo assembly, and a few tied to individual simulations ("unique"), which likely reflect subgrid and parametrization choices such as merger-triggered burst prescriptions. A notable exception is a common population of rapidly quenched galaxies found across most simulations, likely driven by e.g., active galactic nucleus (AGN) feedback, that halo assembly alone cannot reproduce. The origin of the most distinctive unique shape, an Illustris-dominated history with an early peak and slow decline, remains unclear, but we test the effects of short-term variability. With a multi-objective auto-encoder neural network, we recover the simulation that a given sSFH is drawn from with ∼95% out-of-sample accuracy; ablation tests indicate this information is embedded in long-timescale sSFH morphology rather than short-term variability. These results provide avenues for theoretical and observational follow-up connecting star formation histories to subgrid physical models.
Binary systems are extremely common and influence many areas of astrophysics, such as stellar evolution and cluster dynamics. In this work, we introduce flexAE, a framework that combines a classical autoencoder architecture with a dedicated classifier component. It is designed to distinguish between single stars and binary systems based on their broadband spectral energy distributions, using all available photometry simultaneously while accounting for observational uncertainties. We demonstrate the capability of flexAE by using it to identify unresolved main sequence binaries in the outskirts of the 47 Tucanae globular cluster (NGC 104) with photometric data from the Vera C. Rubin Observatory. We train the model on a simulated sample of single star and binary systems created with stellar atmosphere models. The model accurately reconstructs the photometric input features and achieves a classification accuracy of 0.85 on the simulated test set. We then apply the trained model to a sample of 1,424 cluster members found in Rubin DP1 with reliable gri photometry, of which 32 are identified by the model as unresolved main sequence binaries. This corresponds to an observed binary fraction of 2.2+0.5-0.3%. We estimate that the intrinsic main sequence binary fraction lies between 3.7+0.8-0.5% and 7.5+1.5-1.1%. The binary fraction stays constant between 18-36 arcmin (5.7-11.4 half-light radii) from the cluster center. We highlight that flexAE can be easily adapted for other use cases. This makes flexAE well-suited for large-scale binary classification in upcoming wide-field surveys, such as Rubin LSST. The code is publicly available at https://github.com/tobiasgeron/flexAE.
The recent analyses of DESI DR2 BAO, Planck CMB and various Supernovae datasets have shown preferences for evolving dark energy at various levels of significance, under frequentist model comparison tests. However, the same analysis done with a pure Bayesian model comparison test can lead to the opposite conclusion due to the impact of w_0w_a priors. In contrast to these in-sample model comparison route, we approach the problem along a third axis -- which model better predicts unseen data. We use the Expected Log Predictive Density (ELPD) metric to score the predictiveness of ΛCDM and w_0w_aCDM models, using the leave-one-redshift-block-out cross-validation estimator. The comparison metric ∆ELPD is out-of-sample, unlike ∆χ^2_MAP and Bayes Factor, and insensitive to prior width, unlike the Bayes factor. Aggregated, we find modest preferences for w_0 w_aCDM, primarily driven by the BAO. Decomposing the ∆ELPD score by redshift we find that a single BAO block, LRG2 (z = 0.706), supplies the entire BAO preference. Interestingly, the biggest outlier point, LRG1, contributes little to the model predictive scoring because both ΛCDM and w_0w_aCDM fail to predict the observation by equal amount. On the other hand, all SNIa datasets' ∆ELPD scores are consistent with 0, indicating that at an out-of-distribution predictive level, the w_0w_aCDM tension is entirely driven by one BAO point, not SNIa.
Main sequence stars of spectral types late F through M exhibit systematic variability in photometric light curves, particularly when they are young. Rotational modulation of starspots manifests as quasi-sinusoidal variability, which enables the measurement of rotation periods. Variability can also be stochastic, as in stellar flaring. However, since measurements of stochastic processes depend on the time of observation, they are typically noisier. Considering that different manifestations of variability have unique observational nuances, models that naturally unify these are incredibly useful for stellar characterization. Towards this goal, we have developed EncoTESS: a Time Series Foundation Model (TSFM) trained on a subset of TESS 2-min light curves. EncoTESS is specifically designed to handle the observational noise, heteroskedastic measurements, irregular sampling, and large data gaps common to TESS data. It is also ~1% of the size of a typical literature TSFM, so can be run easily on a modern laptop. EncoTESS encodes light curves into a fixed-size latent parameter space, which can be used to infer physical stellar properties and recovers light curve summary statistics well. EncoTESS outperforms rotation period and variability amplitude as age indicators for stars that have not converged onto the slow rotator sequence yet; broadly these include K and M stars less than ~100 Myr, and M stars less than ~1 Gyr. We focus on age inference as an application of EncoTESS in this work, but other downstream tasks such as stellar classification could also be explored. The architecture of EncoTESS enables its future extension to TESS light curves of all cadences, and additional surveys such as Kepler and the upcoming PLATO mission. The core EncoTESS framework and library of encodings produced for the stars used in this work are publicly available at https://github.com/philvanlane/encotess.
Language AI is rapidly becoming part of the astronomy research ecosystem, prompting research teams to develop policies governing its use. But resources and advice for AI adoption assume that all research groups share the same goals and values. This paper lays out an argument for why there is no single "correct" AI policy for astronomy research groups. Instead, we introduce four research laboratory archetypes with competing research priorities, and we use them to explore how a small laboratory or research group's priorities shape decisions about AI's impact on research productivity, scientist development, scientific integrity, and data governance. Rather than prescribing a universal set of rules, this paper provides a framework for aligning AI policies with a group's scientific values and mission. The objective is to help research leaders decide how Language AI should be used within their particular research environment.
Integral field unit (IFU) spectroscopy provides spatially resolved spectra across galaxies, offering crucial insights into their evolution. However, its high observational cost limits current IFU datasets to ∼ 10^4 objects. We present a multi-modal, probabilistic foundation model that predicts high-resolution spectra with calibrated uncertainties at arbitrary spatial locations within a galaxy directly from broadband images. Built on a masked autoencoder framework, our architecture injects fiber positional encodings and redshift aware wavelength encodings, enabling spatially conditioned predictions. Trained on 4.7 million images and single fiber spectroscopic observations from the Dark Energy Spectroscopic Instrument (DESI) survey, our model exploits the natural variance of fiber placements and the morphological self-similarity of galaxies to achieve IFU-like capabilities without any IFU training data. Predicted emission line flux maps match independent IFU observations from the Mapping Nearby Galaxies at APO (MaNGA) survey, with performance comparable to a supervised baseline trained directly on IFU data.
Globular clusters (GCs) lie scattered around the inner 40% of the virial radius of galaxy clusters, potentially being excellent tracers of the underlying mass distribution. In this paper, we present a statistical method based on assuming that the location of GCs around a galaxy cluster follows an inhomogenous spatial Poisson point process, and we use this method to assess to which galactic component GCs are better tracers of. We apply the method to the galaxy cluster Abell 2744, and we find that the spatial distribution of bright GCs roughly traces the three main interacting clumps in the galaxy cluster, alongside other galaxies with sizeable GC populations. The GC populations are more closely correlated to the predicted mass maps than any other galactic component (Spearman rank coefficients >0.7). A perk of this statistical method is that it allows us to distinguish to which map the agreement is closest to. In particular, we find that the Bright Blue GCs are compatible with the mass map solely derived from weak lensing, suggesting that they can provide complementary and independent information on the mass distribution in galaxy clusters with a similar level of detail to that of weak lensing. This statistical method is available in a public repository, and combined with catalogs of GCs in galaxy clusters at different cosmic epochs, it provides an independent method for investigating the mass distribution in these galactic environments.
Constraining the major contributors to the ionisation of the early universe is an ongoing endeavour of high-redshift galaxy research. We measure the ionising photon production efficiency and Lyman continuum escape fraction for a sample of 230 intermediate redshift ( EW and stellar mass.
Accurately forecasting the peak bloom of cherry blossoms requires integrating complex interactions between regional climate, seasonal trends, and biological timing. This paper explores the potential of the tabular prior-data fitted network (TabPFN), a foundation model for tabular data, that can be fine-tuned to predict the cherry blossom peak bloom date in Toronto’s High Park. The network is first trained on global phenological datasets spanning Japan, North America, Switzerland, and South Korea to capture broad temporal and environmental patterns. To improve local adaptation, the data are enriched with year-specific climatic features derived from the Open-Meteo Historical Weather API, including mean spring temperature, cumulative growing degree days, winter chill days, and spring precipitation. Using a leave-one-location-out fine-tuning approach, Toronto is excluded from training, and its predictive performance is evaluated via cross-regional generalization. The results show that climate-informed TabPFN improves Toronto cherry blossom prediction bias by 79.4% compared to a geography-only model and achieves R2= 0.186, with spring temperature and growing degree days as key drivers.
The measured ages of massive, quiescent galaxies at z ∼ 3─4 imply that massive galaxies quench as early as z ∼ 6. While the number of spectroscopic confirmations of quiescent galaxies at z < 3 has increased over the years, there are only a handful at z > 3.5. We report spectroscopic redshifts of one secure (z = 3.757) and two tentative (z = 3.336 and z = 4.673) massive (<inline-formula> </inline-formula>) quiescent galaxies with 11 hr of Keck/MOSFIRE K-band observations. Our candidates were selected from the FLAMINGOS-2 Extragalactic Near-Infrared K-band Split (FENIKS) survey, which uses deep Gemini/Flamingos-2 K b K r imaging optimized for increased sensitivity to the characteristic red colors of galaxies at z > 3 with a strong Balmer/4000 Å break. The rest-frame UVJ and (ugi) s colors of three out of four quiescent candidates are consistent with 1─2 Gyr old stellar populations. This places these galaxies as the oldest objects at these redshifts, and challenges the notion that quiescent galaxies at z > 3 are all recently quenched, post-starburst galaxies. Our spectroscopy shows that the other quiescent-galaxy candidate is a broad-line active galactic nucleus (z = 3.594) with strong, redshifted Hβ + [O III] emission with a velocity offset > 1000 km s−1, indicative of a powerful outflow. The star formation history of our highest redshift candidate suggests that its progenitor was already in place by z ∼ 7─11, reaching ∼1011 M ⊙ by z ≃ 8. These observations reveal the limit of what is possible with deep near-infrared photometry and targeted spectroscopy from the ground and demonstrate that secure spectroscopic confirmation of quiescent galaxies at z > 4 is feasible only with JWST.
We present a deep, high-angular-resolution 3D dust map of the southern Galactic plane over 239° < l < 6° and ∣ b∣ < 10° built on photometry from the DECaPS2 survey, in combination with photometry from VISTA Variables in the Via Lactea, the Two Micron All Sky Survey, and "Unofficial" Wide-field Infrared Survey Explorer and parallaxes from Gaia Data Release 3 where available. To construct the map, we first infer the distance, extinction, and stellar types of over 700 million stars using the BRUTUS stellar inference framework with a set of theoretical MESA Isochrone and Stellar Tracks (MIST) stellar models. Our resultant 3D dust map has an angular resolution of <inline-formula> <mml:math><mml:mn>1</mml:mn><mml:mo>'</mml:mo><mml:mtext></mml:mtext><mml:mspace></mml:mspace><mml:mtext></mml:mtext><mml:mspace></mml:mspace></mml:math> </inline-formula>, roughly an order of magnitude finer than existing 3D dust maps and comparable to the angular resolution of the Herschel 2D dust emission maps. We detect complexes at the range of distances associated with the Sagittarius-Carina and Scutum-Centaurus arms in the fourth quadrant, as well as more distant structures out to a maximum reliable distance of d ≍ 10 kpc from the Sun. The map is sensitive up to a maximum extinction of roughly A V ≍ 12 mag. We publicly release both the stellar catalog and the 3D dust map, the latter of which can easily be queried via the Python package dustmaps. When combined with the existing Bayestar19 3D dust map of the northern sky, the DECaPS 3D dust map fills in the missing piece of the Galactic plane, enabling extinction corrections over the entire disk ∣b∣ < 10°. Our map serves as a pathfinder for the future of 3D dust mapping in the era of LSST and Roman, targeting regimes accessible with deep optical and near-infrared photometry but often inaccessible with Gaia.
We present an overview of the MINERVA survey, a 259.8 hour (prime) and 127 hour (parallel) Cycle 4 treasury program on the James Webb Space Telescope (JWST). MINERVA is obtaining 8 filter NIRCam medium band imaging (F140M, F162M, F182M, F210M, F250M, F300M, F360M, F460M) and 2 filter MIRI imaging (F1280W, F1500W) in four of the five CANDELS Extragalactic fields: UDS, COSMOS, AEGIS and GOODS-N. These fields were previously observed in Cycle 1 with 7 - 9 NIRCam filters by the PRIMER, CEERS and JADES programs. MINERVA reaches a 5σ depth of 28.1 mag in F300M and covers ∼ 542 arcmin^2, increasing the area of existing JWST medium-band coverage in at least 8 bands by ∼ 7×. The MIRI imaging reaches a 5σ depth of 23.9 mag in F1280W and covers ∼ 275 arcmin^2 in at least 2 MIRI filters. When combined with existing imaging, these data will provide a photometric catalog with 20-26 JWST filters (depending on field) and 26-35 filters total, including HST. This paper presents a detailed breakdown of the filter coverage, exposure times, and field layout relative to previous observations, as well as an overview of the primary science goals of the project. These include uncovering the physics of enigmatic sources hiding in current broadband catalogs, improving systematics on stellar mass functions and number densities by factors of ≳ 3, and resolved mapping of stellar mass and star formation at 1 < z < 6. When complete, MINERVA will become an integral part of the treasury deep field imaging datasets, significantly improving population studies with well-understood completeness, robust photometric redshifts, stellar masses, and sizes, and facilitating spectroscopic follow up for decades to come.
Measuring the growth of structure is a powerful probe for studying the dark sector, especially in light of the σ 8 tension between primary CMB anisotropy and low-redshift surveys. This paper provides a new measurement of the amplitude of the matter power spectrum, σ 8, using galaxy-galaxy and galaxy-CMB lensing power spectra of Dark Energy Spectroscopic Instrument Legacy Imaging Surveys Emission-Line Galaxies and the Planck 2018 CMB lensing map. We create an ELG catalog composed of 24 million galaxies and with a purity of 85%, covering a redshift range 0 < z < 3, with z mean = 1.09. We implement several novel systematic corrections, such as jointly modeling the contribution of imaging systematics and photometric redshift uncertainties to the covariance matrix. We also study the impacts of various dust maps on cosmological parameter inference. We measure the cross-power spectra over f sky = 0.25 with a signal-to-background ratio of up to 30σ. We find that the choice of dust maps to account for imaging systematics in estimating the ELG overdensity field has a significant impact on the final estimated values of σ 8 and ΩM, with far-infrared emission-based dust maps preferring σ 8 to be as low as 0.702 ± 0.030, and stellar-reddening-based dust maps preferring as high as 0.719 ± 0.030. The highest preferred value is at ∼ 3 σ tension with the Planck primary anisotropy results. These findings indicate a need for tomographic analyses at high redshifts and joint modeling of systematics.
Gyrochronology is a technique for constraining stellar ages using rotation periods, which change over a star’s main-sequence (MS) lifetime due to magnetic braking. This technique shows promise for MS FGKM stars, where other methods are imprecise. However, the observed dispersion in rotation rates for similar coeval stars has historically been difficult to characterize. To properly understand this complexity, we have assembled the largest standardized data catalog of rotators in open clusters to date, consisting of ≈ 7600 stars across 30 open clusters/associations spanning ages of 1.5 Myr to 4 Gyr. We have also developed ChronoFlow: a flexible data-driven model that accurately captures observed rotational dispersion. We show that ChronoFlow can be used to accurately forward-model rotational evolution, and to infer both cluster and individual stellar ages. We recover cluster ages with a statistical uncertainty of 0.06 dex (≈15%), and individual stellar ages with a statistical uncertainty of 0.7 dex. Additionally, we conducted robust systematic tests to analyze the impact of extinction models, cluster membership, and calibration ages. These contribute an additional 0.06 dex of uncertainty in cluster age estimates, resulting in a total error budget of 0.08 dex. We apply ChronoFlow to estimate ages for M34, NGC 2516, NGC 6709, and the Theia 456 stellar stream. Our results show that ChronoFlow can precisely estimate the ages of coeval stellar populations, and constrain ages for individual stars. Furthermore, its predictions may be used to inform physical spin-down models. ChronoFlow is publicly available online ( https://github.com/philvanlane/chronoflow ).
Data-driven models for stellar spectra that depend on stellar labels suffer from label systematics which decrease model performance: the stellar labels gap. To close the stellar labels gap, we present a stellar label independent model for Gaia BP/RP spectra. We develop a novel implementation of a variational auto-encoder, which learns to generate an XP spectrum and accompanying scatter without relying on stellar labels. We demonstrate that our model achieves competitive XP spectra reconstructions in comparison to stellar label dependent models. We find that our model learns stellar properties directly from the data itself. We then apply our model to XP/APOGEE giant stars to study the [ α / M ] information in Gaia XP. We provide strong evidence that the XP spectra contain meaningful [ α / M ] information by demonstrating that our model learns the α -bimodality, without relying on stellar label correlations for stars with T eff < 5000 K, while also being sensitive to the anomalous abundances of Gaia-Enceladus stars. We have publicly released our trained model, codebase and data. Importantly, our stellar label independent model can be implemented for any and all XP spectra because our model's performance scales with training object density, not training label density.
Star formation in galaxies is regulated by the interplay of a range of processes that shape the multiphase gas in the interstellar and circumgalactic media. Using the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) suite of cosmological simulations, we study the effects of varying feedback and cosmology on the average star formation histories (SFHs) of galaxies at z ∼ 0 across the IllustrisTNG, SIMBA, and ASTRID galaxy formation models. We find that galaxy SFHs in all three models are sensitive to changes in stellar feedback, which affect the efficiency of baryon cycling and the rates at which central black holes grow, whereas the effects of varying active galactic nucleus (AGN) feedback depend on model-specific implementations of black hole seeding, accretion, and feedback. We also find strong interaction terms that couple stellar and AGN feedback, usually by regulating the amount of gas available for the central black hole to accrete. Using a double power law to describe the average SFHs, we derive a general set of equations relating the shape of the SFHs to physical quantities like baryon fraction and black hole mass across all three models. We find that a single set of equations (albeit with different coefficients) can describe the SFHs across all three CAMELS models, with cosmology dominating the SFH at early times, followed by halo accretion, and feedback and baryon cycling at late times. Galaxy SFHs provide a novel, complementary probe to constrain cosmology and feedback, and can connect the observational constraints from current and upcoming galaxy surveys with the physical mechanisms responsible for regulating galaxy growth and quenching.
We present MArk-dependently THinned POint Process (MATHPOP), a novel method to infer the globular cluster (GC) counts in ultra-diffuse galaxies (UDGs) and low-surface brightness galaxies (LSBGs). Many known UDGs have a surprisingly high ratio of GC number to surface brightness. However, standard methods to infer GC counts in UDGs face various challenges, such as photometric measurement uncertainties, GC membership uncertainties, and assumptions about the GC luminosity functions (GCLFs). MATHPOP tackles these challenges using the mark-dependent thinned point process, enabling joint inference of the spatial and magnitude distributions of GCs. In doing so, MATHPOP allows us to infer and quantify the uncertainties in both GC counts and GCLFs with minimal assumptions. As a precursor to MATHPOP, we also address the data uncertainties coming from the selection process of GC candidates: we obtain probabilistic GC candidates instead of the traditional binary classification based on the color─magnitude diagram. We apply MATHPOP to 40 LSBGs in the Perseus cluster using GC catalogs from a Hubble Space Telescope imaging program. We then compare our results to those from an independent study using the standard method. We further calibrate and validate our approach through extensive simulations. Our approach reveals two LSBGs having GCLF turnover points much brighter than the canonical value with Bayes' factor being ∼4.5 and ∼2.5, respectively. An additional crude maximum-likelihood estimation and simulation study show that their GCLF TO points are approximately 0.9 mag and 1.1 mag brighter than the canonical value, with p-values of ∼10−8 and ∼10−5, respectively.
We present a hidden Markov model (HMM) for discovering stellar flares in light-curve data of stars. HMMs provide a framework to model time series data that are nonstationary; they allow for systems to be in different states at different times and consider the probabilities that describe the switching dynamics between states. In the context of the discovery of stellar flares, we exploit the HMM framework by allowing the light curve of a star to be in one of three states at any given time step: quiet, firing, or decaying. This three-state HMM formulation is designed to enable straightforward identification of stellar flares, their duration, and associated uncertainty. This is crucial for estimating the flare's energy, and is useful for studies of stellar flare energy distributions. We combine our HMM with a celerite model that accounts for quasiperiodic stellar oscillations. Through an injection recovery experiment, we demonstrate and evaluate the ability of our method to detect and characterize flares in stellar time series. We also show that the proposed HMM flags fainter and lower energy flares more easily than traditional sigma-clipping methods. Lastly, we visually demonstrate that simultaneously conducting detrending and flare detection can mitigate biased estimations arising in multistage modeling approaches. Thus, this method paves a new way to calculate stellar flare energy. We conclude with an example application to one star observed by TESS, showing how the HMM compares with sigma clipping when using real data.
Star formation in galaxies is regulated by the interplay of a range of processes that shape the multiphase gas in the interstellar and circumgalactic media. Using the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) suite of cosmological simulations, we study the effects of varying feedback and cosmology on the average star formation histories (SFHs) of galaxies at z ∼ 0 across the IllustrisTNG, SIMBA, and ASTRID galaxy formation models. We find that galaxy SFHs in all three models are sensitive to changes in stellar feedback, which affect the efficiency of baryon cycling and the rates at which central black holes grow, whereas the effects of varying active galactic nucleus (AGN) feedback depend on model-specific implementations of black hole seeding, accretion, and feedback. We also find strong interaction terms that couple stellar and AGN feedback, usually by regulating the amount of gas available for the central black hole to accrete. Using a double power law to describe the average SFHs, we derive a general set of equations relating the shape of the SFHs to physical quantities like baryon fraction and black hole mass across all three models. We find that a single set of equations (albeit with different coefficients) can describe the SFHs across all three CAMELS models, with cosmology dominating the SFH at early times, followed by halo accretion, and feedback and baryon cycling at late times. Galaxy SFHs provide a novel, complementary probe to constrain cosmology and feedback, and can connect the observational constraints from current and upcoming galaxy surveys with the physical mechanisms responsible for regulating galaxy growth and quenching.
We present brutus, an open source Python package for quickly deriving stellar properties, distances, and reddenings to stars based on grids of stellar models constrained by photometric and astrometric data. We outline the statistical framework for deriving these quantities, its implementation, and various Galactic priors over the 3-D distribution of stars, stellar properties, and dust extinction (including R_V variation). We establish a procedure to empirically calibrate MIST v1.2 isochrones by using open clusters to derive corrections to the effective temperatures and radii of the isochrones, which reduces systematic errors on the lower main sequence. We also describe and apply a method to estimate photometric offsets between stellar models and observed data using nearby, low-reddening field stars. We perform a series of tests on mock and real data to examine parameter recovery with MIST under different modeling assumptions, illustrating that brutus is able to recover distances and other stellar properties using optical to near-infrared photometry and astrometry. The code is publicly available at https://github.com/joshspeagle/brutus.
In Patil et al., we developed a power spectrum estimation method, mtNUFFT (or multitaper nonuniform fast Fourier transform), for analyzing time series with quasi-regular spacing, and showed that it not only improves upon the statistical issues of the Lomb─Scargle (LS) periodogram, but also provides a factor of 3 speedup in some applications. In this paper, we extend mtNUFFT to include a multitaper F-test, a hypothesis test to assess whether a strictly periodic signal or its harmonic (as opposed to, e.g., a quasi-periodic signal) is present at a given frequency. This extension is possible because the F-test is an accompaniment to the multitaper power spectrum estimator (as opposed to other estimators such as the LS periodogram). The mtNUFFT/F-test combination allows detection of strictly periodic signals embedded in noise and precise estimation of their frequencies, in addition to power spectrum estimation. Using asteroseismic time-series data for the Kepler-91 red giant, we show that the F-test automatically picks up the harmonics of its transiting exoplanet as well as certain dipole (l = 1) mixed modes. We use this example to highlight that we can distinguish between different types of stellar oscillations, e.g., transient (damped, stochastically excited) and strictly periodic (undamped, heat driven). We also illustrate the technique of dividing a time series into chunks to further examine the transient versus periodic nature of stellar oscillations. The harmonic F-test combined with mtNUFFT is implemented in the public Python package tapify, which opens opportunities to perform detailed investigations of periodic signals in time-domain astronomy.
JWST has revealed unexpectedly bright galaxies in the first 500 Myr after the Big Bang. Their overabundance suggests that they are preferentially observed during burst phases, where their star formation rates increase dramatically. In cosmological simulations, such bursts transition into short (≈ 40 Myr) periods without star formation or naps. Using JWST/NIRCam medium-band observations, we report the discovery of the galaxy CANUCS-A370-2228423 (z = 5.95 ± 0.06, log(M_∗/M⊙) = 9.14 ± 0.09), dubbed The Sleeper. Its star formation history indicates rapid assembly in the first 300 Myr (z ≳ 14), where it formed a log(M_∗/M⊙) = 8.7+0.3-0.4 M⊙ progenitor, comparable in stellar mass to the few spectroscopically confirmed galaxies at those redshifts. Unexpectedly, this is followed by several hundred million years of suppressed star formation, in stark contrast to nappers. This results in a remarkably strong hydrogen Balmer break, exceeding that of any galaxy observed within the first billion years by a factor of ≈ 3. Furthermore, Sleeper-like systems are overabundant in the observed survey volume compared to theory, as the probability of finding such galaxies in simulations is < 0.2%. The discovery of The Sleeper therefore disrupts the current narrative that all luminous galaxies in the first few hundred million years grow into massive descendants. Instead it presents an alternative evolutionary pathway in which these unusually luminous galaxies fade into inefficient dwarfs after an early starburst, revealing greater diversity in the first stages of galaxy evolution.
This decade has seen the first measurements of extrasolar planetary obliquities, characterizing how an exoplanet's spin axis is oriented relative to its orbital axis. These measurements are enabled by combining projected rotational velocities, planetary rotation periods, and astrometric orbits for directly imaged super-Jupiters. This approach constrains both the spin axis and orbital inclination relative to the line of sight, allowing obliquity measurements for individual systems and offering new insights into their formation. To test whether these super-Jupiters form more like scaled-up planets or scaled-down stars, we develop a hierarchical Bayesian framework to infer their population-level obliquity distribution. Using a single-parameter Fisher distribution, we compare two models: a planet-like formation scenario (κ = 5) predicting moderate alignment, versus a brown-dwarf-like formation scenario (κ = 0) predicting isotropic obliquities. Based on a sample of four young super-Jupiter systems, we find early evidence favoring the isotropic case with a Bayes factor of 15, consistent with turbulent fragmentation.
Recent Gaia-based young stellar association surveys have revealed dozens of low-mass populations that have, until recently, been too small or sparse to detect. These populations represent a largely unstudied demographic with unknown origins, and their relative isolation may minimize gravitational disruptions that impact traceback, making them compelling targets for dynamical studies. In this paper, we survey 15 of these isolated young associations for the first time: Andromeda South (SCYA-97), Aquila East, Aries South (SCYA-104), Cassiopeia East (SCYA-43), Canis Major North, Leo Central (SCYA-2), Leo East (SCYA-3), Theia 72, Ophiuchus Southeast, Scutum North (SCYA-70), Taurus-Orion 1 (TOR1), Theia 78, Vulpecula East (UPK 88), SCYA-54, and SCYA-79. By combining Gaia astrometry and photometry with new ground-based spectroscopic measurements, we assess the membership of each population, search for substructure, analyze their demographics, and compute ages. We find that the smallest populations in our sample contain <20 M⊙ of stellar mass, making them the smallest associations ever detected. Four host substantial substructure, including TOR1, where we discover TOR1B, a new 16 M⊙ association with radial velocities inconsistent with an origin in the parent complex. Using PARSEC isochrones, we produce self-consistent ages for all populations supported by dynamical and lithium depletion ages, which range from 6.9 ± 0.5 Myr in TOR1A to 42.8 ± 2.4 in AndS. Our results provide the first detailed overview of the properties of these populations, characterizing a largely unknown category of young associations that may have an important role in tracing the processes that guide local star formation.
We introduce Galaxy Zoo Evo, a labeled dataset for building and evaluating foundation models on images of galaxies. GZ Evo includes 104M crowdsourced labels for 823k images from four telescopes. Each image is labeled with a series of fine-grained questions and answers (e.g. "featured galaxy, two spiral arms, tightly wound, merging with another galaxy"). These detailed labels are useful for pretraining or finetuning. We also include four smaller sets of labels (167k galaxies in total) for downstream tasks of specific interest to astronomers, including finding strong lenses and describing galaxies from the new space telescope Euclid. We hope GZ Evo will serve as a real-world benchmark for computer vision topics such as domain adaption (from terrestrial to astronomical, or between telescopes) or learning under uncertainty from crowdsourced labels. We also hope it will support a new generation of foundation models for astronomy; such models will be critical to future astronomers seeking to better understand our universe.
Milky Way analogs (MWAs) have long been studied by astronomers to place our Galaxy within an extragalactic context. With the power of cosmological simulations, we are now able to not only characterize MWAs today, but also watch as they evolve through cosmic time. We use the Evolution and Assembly of GaLaxies and their Environments (EAGLE) and Illustris The Next Generation (IllustrisTNG) simulations to study a group of MWAs defined by their stellar mass (SM) and star formation rate (SFR). We trace these galaxies back along their evolution to investigate the star-forming and mass assembly tracks taken by a galaxy to become an MWA today in light of these chosen parameters. We also take mock-observations of “MWAs” at z > 0 and trace them forward in time to determine if galaxies that looked similar to the Milky Way earlier in their evolution still look like the Milky Way today, thus quantifying a selection …
Spectral data reduction pipelines deal with a wide variety of challenges including masking cosmic rays, calibrating wavelength solutions, and estimating background noise while trying to remain model-agnostic. Traditional methods rely on hardware-specific code or pre-calculated stellar model templates to solve this problem, making them model-dependent and not suitable for large datasets that may contain new classes of objects. To solve this problem, we present a flexible, data-driven method: the GausSian PIxelwise Conditional Estimator (GSPICE) that models an ensemble of spectra as a multivariate Gaussian and estimates the expected value and expected variance of each pixel in each spectrum conditional on others. GSPICE compares observed fluxes and errors to its own flux and error estimates to reveal outliers, which then can be completely masked or replaced by their estimates. We apply GSPICE to 3.9 million stellar spectra from the LAMOST survey, and show that variations of the method can directly identify and correct both individual pixel-level outliers (e.g., from cosmic ray hits) as well as extended systematic features (e.g., from incorrect wavelength calibrations), while still providing a novel characterization of the true per-pixel measurement uncertainties. We also demonstrate how GSPICE can take advantage of data partitioning with an application to diffuse interstellar bands. Implementations of GSPICE in both Python and IDL can be found here http://github.com/dfink/gspice.
Context. High-resolution 3D maps of interstellar dust are critical for probing the underlying physics shaping the structure of the interstellar medium, and for foreground correction of astrophysical observations affected by dust. <BR /> Aims: We aim to construct a new 3D map of the spatial distribution of interstellar dust extinction out to a distance of 1.25 kpc from the Sun. <BR /> Methods: We leveraged distance and extinction estimates to 54 million nearby stars derived from the Gaia BP/RP spectra. Using the stellar distance and extinction information, we inferred the spatial distribution of dust extinction. We modeled the logarithmic dust extinction with a Gaussian process in a spherical coordinate system via iterative charted refinement and a correlation kernel inferred in previous work. In total, our posterior has over 661 million degrees of freedom. We probed the posterior distribution using the variational inference method MGVI. <BR /> Results: Our 3D dust map has an angular resolution of up to 14′ (Nside = 256), and we achieve parsec-scale distance resolution, sampling the dust in 516 logarithmically spaced distance bins spanning 69 pc to 1250 pc. We generated 12 samples from the variational posterior of the 3D dust distribution and release the samples alongside the mean 3D dust map and its corresponding uncertainty. <BR /> Conclusions: Our map resolves the internal structure of hundreds of molecular clouds in the solar neighborhood and will be broadly useful for studies of star formation, Galactic structure, and young stellar populations. It is available for download in a variety of coordinate systems online and can also be queried via the publicly available dustmaps Python package. <P />A movie and a 3D interactive figure associated with Fig. 5 are available at <A href="https://www.aanda.org/10.1051/0004-6361/202347628/olm">https://aanda.org</A>
Galaxy formation and evolution involve a variety of effectively stochastic processes that operate over different timescales. The extended regulator model provides an analytic framework for the resulting variability (or "burstiness") in galaxy-wide star formation due to these processes. It does this by relating the variability in Fourier space to the effective timescales of stochastic gas inflow, equilibrium, and dynamical processes influencing giant molecular clouds' creation and destruction using the power spectral density (PSD) formalism. We use the connection between the PSD and autocovariance function for general stochastic processes to reformulate this model as an autocovariance function, which we use to model variability in galaxy star formation histories (SFHs) using physically motivated Gaussian processes in log star formation rate (SFR) space. Using stellar population synthesis models, we then explore how changes in model stochasticity can affect spectral signatures across galaxy populations with properties similar to the Milky Way and present-day dwarfs, as well as at higher redshifts. We find that, even at fixed scatter, perturbations to the stochasticity model (changing timescales vs. overall variability) leave unique spectral signatures across both idealized and more realistic galaxy populations. Distributions of spectral features including Hα and UV-based SFR indicators, Hδ and Ca H and K absorption-line strengths, D n (4000), and broadband colors provide testable predictions for galaxy populations from present and upcoming surveys with the Hubble Space Telescope, James Webb Space Telescope, and Nancy Grace Roman Space Telescope. The Gaussian process SFH framework provides a fast, flexible implementation of physical covariance models for the next generation of spectral energy distribution modeling tools. Code to reproduce our results can be found at https://github.com/kartheikiyer/GP-SFH.
We present the first systematic investigation of supervised scaling laws outside of an ImageNet-like context - on images of galaxies. We use 840k galaxy images and over 100M annotations by Galaxy Zoo volunteers, comparable in scale to Imagenet-1K. We find that adding annotated galaxy images provides a power law improvement in performance across all architectures and all tasks, while adding trainable parameters is effective only for some (typically more subjectively challenging) tasks. We then compare the downstream performance of finetuned models pretrained on either ImageNet-12k alone vs. additionally pretrained on our galaxy images. We achieve an average relative error rate reduction of 31% across 5 downstream tasks of scientific interest. Our finetuned models are more label-efficient and, unlike their ImageNet-12k-pretrained equivalents, often achieve linear transfer performance equal to that of end-to-end finetuning. We find relatively modest additional downstream benefits from scaling model size, implying that scaling alone is not sufficient to address our domain gap, and suggest that practitioners with qualitatively different images might benefit more from in-domain adaption followed by targeted downstream labelling.
We present the first detailed chemical-abundance analysis of stars from the dwarf-galaxy stellar stream Wukong/LMS-1 covering a wide metallicity range (-3.5 < [Fe/H] ≲ -1.3). We find abundance patterns that are effectively indistinguishable from the bulk of Indus and Jhelum, a pair of smaller stellar streams proposed to be dynamically associated with Wukong/LMS-1. We confirmed a carbon-enhanced metal-poor star ( [C/Fe] > +0.7 and [Fe/H] ∼ -2.9) in Wukong/LMS-1 with strong enhancements in Sr, Y, and Zr, which is peculiar given its solar-level [Ba/Fe]. Wukong/LMS-1 stars have high abundances of α elements up to [Fe/H] ≳ -2, which is expected for relatively massive dwarfs. Towards the high-metallicity end, Wukong/LMS-1 becomes α-poor, revealing that it probably experienced fairly standard chemical evolution. We identified a pair of N- and Na-rich stars in Wukong/LMS-1, reminiscent of multiple stellar populations in globular clusters. This indicates that this dwarf galaxy contained at least one globular cluster that was completely disrupted in addition to two intact ones previously known to be associated with Wukong/LMS-1, which is possibly connected to similar evidence found in Indus. From these ≥3 globular clusters, we estimate the total mass of Wukong/LMS-1 to be {≈ }10¹⁰ M_⊙, representing ∼1 per cent of the present-day Milky Way. Finally, the [Eu/Mg] ratio in Wukong/LMS-1 continuously increases with metallicity, making this the first example of a dwarf galaxy where the production of r-process elements is clearly dominated by delayed sources, presumably neutron-star mergers.
We present initial results from the Dark Energy Spectroscopic Instrument (DESI) complete calibration of the colour-redshift relation (DC3R2) secondary target survey. Our analysis uses 230 k galaxies that overlap with KiDS-VIKING ugriZYJHKs photometry to calibrate the colour-redshift relation and to inform photometric redshift (photo-z) inference methods of future weak lensing surveys. Together with emission line galaxies (ELGs), luminous red galaxies (LRGs), and the Bright Galaxy Survey (BGS) that provide samples of complementary colour, the DC3R2 targets help DESI to span 56 per cent of the colour space visible to Euclid and LSST with high confidence spectroscopic redshifts. The effects of spectroscopic completeness and quality are explored, as well as systematic uncertainties introduced with the use of common Self-Organizing Maps trained on different photometry than the analysis sample. We further examine the dependence of redshift on magnitude at fixed colour, important for the use of bright galaxy spectra to calibrate redshifts in a fainter photometric galaxy sample. We find that noise in the KiDS-VIKING photometry introduces a dominant, apparent magnitude dependence of redshift at fixed colour, which indicates a need for carefully chosen deep drilling fields, and survey simulation to model this effect for future weak lensing surveys.
Close binary systems are the progenitors to both Type Ia supernovae and the compact object mergers that can be detected via gravitational waves. To achieve a binary with a small radial separation, it is believed that the system likely undergoes common envelope (CE) evolution. Despite its importance, CE evolution may be one of the largest uncertainties in binary evolution due to a combination of computational challenges and a lack of observed benchmarks where both the post-CE and pre-CE conditions are known. Identifying post-CE systems in star clusters can partially circumvent this second issue by providing an independent age constraint on the system. For the first time, we conduct a systematic search for white dwarf and main-sequence binary systems in 299 Milky Way open star clusters. Coupling Gaia DR3 photometry and kinematics with multiband photometry from Pan-STARRS1 and the Two Micron All Sky Survey, we apply a machine learning-based approach and find 52 high-probability candidates in 38 open clusters. For a subset of our systems, we present follow-up spectroscopy from the Gemini and Lick Observatories and archival light curves from the Transiting Exoplanet Survey Satellite, Kepler/K2, and the Zwicky Transient Facility. Examples of M dwarfs with hot companions are spectroscopically observed, along with regular system variability. While the kinematics of our candidates are consistent with their host clusters, some systems have spatial positions offset relative to their hosts, potentially indicative of natal kicks. Ultimately, this catalog is a first step to obtaining a set of observational benchmarks to better link post-CE systems to their pre-CE progenitors.
The amount of power contained in the variations in galaxy star-formation histories (SFHs) across a range of time-scales encodes key information about the physical processes which modulate star formation. Modelling the SFHs of galaxies as stochastic processes allows the relative importance of different time-scales to be quantified via the power spectral density (PSD). In this paper, we build upon the PSD framework and develop a physically motivated, ‘stochastic’ prior for non-parametric SFHs in the spectral energy distribution (SED)-modelling code prospector. We test this prior in two different regimes: (1) massive, z = 0.7 galaxies with both photometry and spectra, analogous to those observed with the LEGA-C survey, and (2) z = 8 galaxies with photometry only, analogous to those observed with NIRCam on JWST. We find that it is able to recover key galaxy parameters (e.g. stellar mass, stellar metallicity) to the same level of fidelity as the commonly used continuity prior. Furthermore, the realistic variability information incorporated by the stochastic SFH model allows it to fit the SFHs of galaxies more accurately and precisely than traditional non-parametric models. In fact, the stochastic prior is ≳ 2× more accurate than the continuity prior in measuring the recent star-formation rates (log SFR₁₀₀ and log SFR₁₀) of both the z = 0.7 and z = 8 mock systems. While the PSD parameters of individual galaxies are difficult to constrain, the stochastic prior implementation presented in this work allows for the development of hierarchical models in the future, i.e. simultaneous SED-modelling of an ensemble of galaxies to measure their underlying PSD.
This work presents the Globular cluster Extra-tidal Mock Star (GEMS) catalogue of extra-tidal stars and binaries created via three-body dynamical encounters in globular cluster cores. Using the particle-spray code Corespray, we sample N=50 000 extra-tidal stars and escaped recoil binaries for 159 Galactic globular clusters. Sky positions, kinematics, stellar properties, and escape information are provided for all simulated stars. Stellar orbits are integrated in seven different static and time-varying Milky Way gravitational potential models where the structure of the disc, perturbations from the Large Magellanic Cloud and the mass and sphericity of the Milky Way’s dark matter halo are all investigated. We find that the action coordinates of the mock extra-tidal stars are largely Galactic model independent, where minor offsets and broadening of the distributions between models are likely due to interactions with substructure. Importantly, we also report the first evidence for stellar stream contamination by globular cluster core stars and binaries for clusters with pericentre radii larger than five kiloparsecs. Finally, we provide a quantitative tool that uses action coordinates to match field stars to host clusters with probabilities. Ultimately, combining data from the GEMS catalogue with information of observed stars will allow for association of extra-tidal field stars with any Galactic globular cluster; a requisite tool for understanding population-level dynamics and evolution of clusters in the Milky Way.
Blue horizontal branch stars (BHBs), excellent distant tracers for probing the Milky Way's halo density profile, are distinguished in the <inline-formula> <mml:math overflow="scroll"><mml:msub><mml:mrow><mml:mfenced close=")" open="("><mml:mrow><mml:mi>g</mml:mi><mml:mo>−</mml:mo><mml:mi>r</mml:mi></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> </inline-formula> versus (i − z)0 color space from another class of stars, blue straggler stars. We develop a Bayesian mixture model to classify BHBs using high-precision photometry data from the Dark Energy Survey Data Release 2 (DES DR2). We select ∼2100 highly probable BHBs based on their griz photometry and the associated uncertainties, and we use these stars to map the stellar halo over the Galactocentric radial range 20 kpc ≲ R ≲ 70 kpc. After excluding known stellar overdensities, we find that the number density n ⋆ of BHBs can be represented by a power-law density profile n ⋆ ∝ R −α with an index of <inline-formula> <mml:math overflow="scroll"><mml:mi>α</mml:mi><mml:mo>=</mml:mo><mml:msubsup><mml:mrow><mml:mn>4.34</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>0.12</mml:mn></mml:mrow><mml:mrow><mml:mo>+</mml:mo><mml:mn>0.13</mml:mn></mml:mrow></mml:msubsup><mml:mo>±</mml:mo><mml:mn>0.52</mml:mn></mml:math> </inline-formula>, consistent with existing literature values. In addition, we examine the impact of systematic errors and the spatial inhomogeneity on the fitted density profile. Our work demonstrates the effectiveness of high-precision griz photometry in selecting BHBs. The upcoming photometric survey from the Rubin Observatory, expected to reach depths 2─3 mag greater than DES during its 10 yr mission, will enable us to investigate the density profile of the Milky Way's halo out to the virial radius, unraveling the complex processes of formation and evolution in our Galaxy.
The physical processes responsible for shaping how galaxies form and quench over time leave imprints on both the spatial (galaxy morphology) and temporal (star formation history; SFH) tracers that we use to study galaxies. While the morphology─SFR connection is well studied, the correlation with past star formation activity is not as well understood. To quantify this, we present Katachi (形), an interpretable convolutional neural network framework that learns the connection between the factors regulating star formation in galaxies on different spatial and temporal scales. Katachi is trained on 9904 galaxies at 0.02 < z < 0.1 in the SDSS-IV MaNGA DR17 sample to predict stellar mass (M *; root mean square error (RSME) 0.22 dex), current star formation rate (SFR; RMSE 0.31 dex), and half-mass time (t 50; RMSE 0.23 dex). This information allows us to reconstruct nonparametric SFHs for each galaxy from gri imaging alone. To quantify the morphological features informing the SFH predictions, we use SHAP (SHapley Additive exPlanations). We recover the expected trends of M * governed by the growth of galaxy bulges, as well as those of SFR correlating with spiral arms and other star-forming regions. We also find that the SHAP maps of D4000 are more complex than those of M * and SFR, and that morphology is correlated with t 50 even at fixed mass and SFR. Katachi serves as a scalable public framework to predict galaxy properties from large imaging surveys including Rubin, Roman, and Euclid, with large data sets of high signal-to-noise ratio imaging across limited photometric bands. * https://github.com/juanpabloalfonzo/Katachi
Asteroseismic time series data have imprints of stellar oscillation modes, whose detection and characterization through time series analysis allows us to probe stellar interior physics. Such analyses usually occur in the Fourier domain by computing the Lomb─Scargle (LS) periodogram, an estimator of the power spectrum underlying unevenly sampled time series data. However, the LS periodogram suffers from the statistical problems of (1) inconsistency (or noise) and (2) bias due to high spectral leakage. Here, we develop a multitaper power spectrum estimator using the nonuniform fast Fourier transform (mtNUFFT) to tackle the inconsistency and bias problems of the LS periodogram. Using a simulated light curve, we show that the mtNUFFT power spectrum estimate of solar-like oscillations has lower variance and bias than the LS estimate. We also apply our method to the Kepler-91 red giant, and combine it with PBjam peakbagging to obtain mode parameters and a derived age estimate of 3.97 ± 0.52 Gyr. PBjam allows the improvement of age precision relative to the 4.27 ± 0.75 Gyr APOKASC-2 (uncorrected) estimate, whereas partnering mtNUFFT with PBjam speeds up peakbagging thrice as much as LS. This increase in efficiency has promising implications for Galactic archaeology, in addition to stellar structure and evolution studies. Our new method generally applies to time-domain astronomy and is implemented in the public Python package tapify, available at https://github.com/aaryapatil/tapify.
We infer the dynamical masses of stars across the Hertzsprung-Russell (H-R) diagram using wide binaries from the Gaia survey. Gaia's high-precision astrometry measures the wide binaries' orbital motion, which contains the mass information. Using wide binaries as the training sample, we measure the mass of stars across the 2D H-R diagram using the combination of statistical inference and neural networks. Our results provide the dynamical mass measurements for main-sequence stars from 0.1 to 2 M☉, unresolved binaries, and unresolved triples on the main sequence, and the mean masses of giants and white dwarfs. Two regions in the H-R diagram show interesting behaviours in mass, where one of them is pre-main-sequence stars, and the other one may be related to close compact object companions like M dwarf-white dwarf binaries. These mass measurements depend solely on Newtonian dynamics with minimal assumptions on eccentricities, providing independent constraints on stellar evolutionary models, and the occurrence rate of compact objects.
In almost any study involving optical/near-infrared photometry, understanding the completeness of detection and recovery is an essential part of the work. The recovery fraction is, in general, a function of several variables including magnitude, color, background sky noise, and crowding. We explore how completeness can be modeled, with the use of artificial-star tests, in a way that includes all of these parameters simultaneously within a neural network (NN) framework. The method is able to manage common issues including asymmetric completeness functions and the bilinear dependence of the detection limit on color index. We test the method with two sample Hubble Space Telescope data sets: the first involves photometry of the star cluster population around the giant Perseus galaxy NGC 1275, and the second involves the halo-star population in the nearby elliptical galaxy NGC 3377. The NN-based method achieves a classification accuracy of > 94% and produces results entirely consistent with more traditional techniques for determining completeness. Additional advantages of the method are that none of the issues arising from the binning of the data are present and that a recovery probability can be assigned to every individual star in real photometry. Our data, models, and code (called COINTOSS) can be found online on Zenodo at the following link: https://doi.org/10.5281/zenodo.8306488.
We present Augustus, a catalog of distance, extinction, and stellar parameter estimates for 170 million stars from 14 mag < r < 20 mag and with ∣b∣ > 10° drawing on a combination of optical to near-infrared photometry from Pan-STARRS, 2MASS, UKIDSS, and unWISE along with parallax measurements from Gaia DR2 and 3D dust extinction maps. After applying quality cuts, we find 125 million objects have "high-quality" posteriors with statistical distance uncertainties of ≲10% for objects with well-constrained stellar types. This is a substantial improvement over the distance estimates derived from Gaia parallaxes alone and in line with the recent results from Anders et al. We find the fits are able to reproduce the dereddened Gaia color─magnitude diagram accurately, which serves as a useful consistency check of our results. We show that we are able to detect large, kinematically coherent substructures in our data clearly relative to the input priors, including the Monoceros Ring and the Sagittarius Stream, attesting to the quality of the catalog. Our results are publicly available at doi:10.7910/DVN/WYMSXV. An accompanying interactive visualization can be found at http://allsky.s3-website.us-east-2.amazonaws.com.
The scaling relation between the size of a galaxy's globular cluster (GC) population (N GC) and the galaxy's stellar mass (M *) is usually described with a continuous, linear model, but in reality it is a count relationship that should be modeled as such. For massive galaxies, a negative binomial (NB) model has been shown to describe the data well, but it is unclear how the scaling relation behaves at low galaxy masses where a substantial portion of galaxies have N GC = 0. In this work, we test the utility of Poisson and NB models for describing the low-mass end of the N GC−M * scaling relation. We introduce the use of zero-inflated versions of these models, which allow for larger zero populations (e.g., galaxies without GCs) than would otherwise be predicted. We evaluate our models with a variety of predictive model comparison methods, including predictive intervals, the leave-one-out cross-validation criterion, and posterior predictive comparisons. We find that the NB model is consistent with our data, but the naive Poisson is not. Moreover, we find that zero inflation of the models is not necessary to describe the population of low-mass galaxies that lack GCs, suggesting that a single formation and evolutionary process acts over all galaxy masses. Under the NB model, there does not appear to be anything unique about the lack of GCs in many low-mass galaxies; they are simply the low-mass extension of the larger N GC−M * scaling relation.
We present a hierarchical Bayesian inference approach to estimating the structural properties and the phase-space centre of a globular cluster (GC) given the spatial and kinematic information of its stars based on lowered isothermal cluster models. As a first step towards more realistic modelling of GCs, we built a differentiable, accurate emulator of the lowered isothermal distribution function using interpolation. The reliable gradient information provided by the emulator allows the use of Hamiltonian Monte Carlo methods to sample large Bayesian models with hundreds of parameters, thereby enabling inference on hierarchical models. We explore the use of hierarchical Bayesian modelling to address several issues encountered in observations of GC including an unknown GC centre, incomplete data, and measurement errors. Our approach not only avoids the common technique of radial binning but also incorporates the aforementioned uncertainties in a robust and statistically consistent way. Through demonstrating the reliability of our hierarchical Bayesian model on simulations, our work lays out the foundation for more realistic and complex modelling of real GC data.
Stellar ages are key for determining the formation history of the Milky Way, but are difficult to measure precisely. Furthermore, methods that use chemical abundances to infer ages may entangle the intrinsic evolution of stars with the chemodynamical evolution of the Galaxy. In this paper, we present a framework for making probabilistic predictions of stellar ages, and then quantify the contribution of both stellar evolution and Galactic chemical evolution to those predictions using SHapley Additive exPlanations. We apply this interpretable prediction framework to both a simulated Milky Way sample containing stars in a variety of evolutionary stages and an APOGEE-mocked sample of red clump stars. We find that in the former case, stellar evolution is the dominant driver for age estimates, while in the latter case, the more restricted evolutionary information causes the model to proxy ages through the chemical evolution model. We show that as a result of the use of nonintrinsic Galactic chemical information, trends estimated with the predicted ages, such as the age-metallicity relation, can deviate from the truth.
Transformers are often the go-to architecture to build foundation models that ingest a large amount of training data. But these models do not estimate the probability density distribution when trained on regression problems, yet obtaining full probabilistic outputs is crucial to many fields of science, where the probability distribution of the answer can be non-Gaussian and multimodal. In this work, we demonstrate that training a probabilistic model using a denoising diffusion head on top of the Transformer provides reasonable probability density estimation even for high-dimensional inputs. The combined Transformer+Denoising Diffusion model allows conditioning the output probability density on arbitrary combinations of inputs and it is thus a highly flexible density function emulator of all possible input/output combinations. We illustrate our Transformer+Denoising Diffusion model by training it on a large dataset of astronomical observations and measured labels of stars within our Galaxy and we apply it to a variety of inference tasks to show that the model can infer labels accurately with reasonable distributions.
With just a month of data, JWST is already transforming our view of the universe, revealing and resolving starlight in unprecedented populations of galaxies. Although "HST-dark" galaxies have previously been detected at long wavelengths, these observations generally suffer from a lack of spatial resolution, which limits our ability to characterize their sizes and morphologies. Here we report on a first view of starlight from a subset of the HST-dark population that is bright with JWST/NIRCam (4.4 μm < 24.5 mag) and very faint or even invisible with HST (<1.6 μm). In this Letter we focus on a dramatic and unanticipated population of physically extended galaxies (≳0.″25). These 12 galaxies have photometric redshifts 2 < z < 6, high stellar masses M ⋆ ≳ 1010 M ☉, and significant dust-attenuated star formation. Surprisingly, the galaxies have elongated projected axis ratios at 4.4 μm, suggesting that the population is disk dominated or prolate and we hence refer to them as ultrared flattened objects. Most of the galaxies appear red at all radii, suggesting significant dust attenuation throughout. With R e (F444W) ~ 1-2 kpc, the galaxies are similar in size to compact massive galaxies at z ~ 2 and the cores of massive galaxies and S0s at z ~ 0. The stellar masses, sizes, and morphologies of the sample suggest that some could be progenitors of lenticular or fast-rotating galaxies in the local universe. The existence of this population suggests that our previous censuses of the universe may have missed massive, dusty edge-on disks, in addition to dust-obscured starbursts.
Deep optical and near-infrared imaging of the entire Galactic plane is essential for understanding our Galaxy’s stars, gas, and dust. The second data release of the Dark Energy Camera (DECam) Plane Survey extends the five-band optical and near-infrared survey of the southern Galactic plane to cover 6.5% of the sky, ∣ b ∣ ≤ 10°, and 6° > ℓ > −124°, complementary to coverage by Pan-STARRS1. Typical single-exposure effective depths, including crowding effects and other complications, are 23.5, 22.6, 22.1, 21.6, and 20.8 mag in g , r , i , z , and Y bands, respectively, with around 1″ seeing. The survey comprises 3.32 billion objects built from 34 billion detections in 21,400 exposures, totaling 260 hr open shutter time on the DECam at Cerro Tololo. The data reduction pipeline features several improvements, including the addition of synthetic source injection tests to validate photometric solutions across the entire survey footprint. A convenient functional form for the detection bias in the faint limit was derived and leveraged to characterize the photometric pipeline performance. A new postprocessing technique was applied to every detection to debias and improve uncertainty estimates of the flux in the presence of structured backgrounds, specifically targeting nebulosity. The images and source catalogs are publicly available at http://decaps.skymaps.info/ .
Flagship near-future surveys targeting 108-109 galaxies across cosmic time will soon reveal the processes of galaxy assembly in unprecedented resolution. This creates an immediate computational challenge on effective analyses of the full data set. With simulation-based inference (SBI), it is possible to attain complex posterior distributions with the accuracy of traditional methods but with a >104 increase in speed. However, it comes with a major limitation. Standard SBI requires the simulated data to have characteristics identical to those of the observed data, which is often violated in astronomical surveys due to inhomogeneous coverage and/or fluctuating sky and telescope conditions. In this work, we present a complete SBI-based methodology, SBI ++ , for treating out-of-distribution measurement errors and missing data. We show that out-of-distribution errors can be approximated by using standard SBI evaluations and that missing data can be marginalized over using SBI evaluations over nearby data realizations in the training set. In addition to the validation set, we apply SBI ++ to galaxies identified in extragalactic images acquired by the James Webb Space Telescope, and show that SBI ++ can infer photometric redshifts at least as accurately as traditional sampling methods-and crucially, better than the original SBI algorithm using training data with a wide range of observational errors. SBI ++ retains the fast inference speed of ~1 s for objects in the observational training set distribution, and additionally permits parameter inference outside of the trained noise and data at ~1 minute per object. This expanded regime has broad implications for future applications to astronomical surveys. (Code and a Jupyter tutorial are made publicly available at https://github.com/wangbingjie/sbi_pp.)
We model the stellar abundances and ages of two disrupted dwarf galaxies in the Milky Way stellar halo: Gaia-Sausage Enceladus (GSE) and Wukong/LMS-1. Using a statistically robust likelihood function, we fit one-zone models of galactic chemical evolution with exponential infall histories to both systems, deriving e-folding time-scales of τin = 1.01 ± 0.13 Gyr for GSE and τ _in = 3.08+3.19-1.16 Gyr for Wukong/LMS-1. GSE formed stars for τ _tot = 5.40+0.32-0.31 Gyr, sustaining star formation for ~1.5-2 Gyr after its first infall into the Milky Way ~10 Gyr ago. Our fit suggests that star formation lasted for τ _tot = 3.36+0.55-0.47 Gyr in Wukong/LMS-1, though our sample does not contain any age measurements. The differences in evolutionary parameters between the two are qualitatively consistent with trends with stellar mass M⋆ predicted by simulations and semi-analytic models of galaxy formation. Our inferred values of the outflow mass-loading factor reasonably match η ∝ M_⋆ -1/3 as predicted by galactic wind models. Our fitting method is based only on Poisson sampling from an evolutionary track and requires no binning of the data. We demonstrate its accuracy by testing against mock data, showing that it accurately recovers the input model across a broad range of sample sizes (20 ≤ N ≤ 2000) and measurement uncertainties (0.01 ≤ σ[α/Fe], σ[Fe/H] ≤ 0.5; 0.02 ≤ σ _{log ₁₀(age)} ≤ 1). Due to the generic nature of our derivation, this likelihood function should be applicable to one-zone models of any parametrization and easily extensible to other astrophysical models which predict tracks in some observed space.
Artificial neural network emulators have been demonstrated to be a very computationally efficient method to rapidly generate galaxy spectral energy distributions, for parameter inference or otherwise. Using a highly flexible and fast mathematical structure, they can learn the nontrivial relationship between input galaxy parameters and output observables. However, they do so imperfectly, and small errors in flux prediction can yield large differences in recovered parameters. In this work, we investigate the relationship between an emulator's execution time, uncertainties, correlated errors, and ability to recover accurate posteriors. We show that emulators can recover consistent results to traditional fits, with a precision of 25%-40% in posterior medians for stellar mass, stellar metallicity, star formation rate, and stellar age. We find that emulation uncertainties scale with an emulator's width N as ∝N -1, while execution time scales as ∝N 2, resulting in an inherent tradeoff between execution time and emulation uncertainties. We also find that emulators with uncertainties smaller than observational uncertainties are able to recover accurate posteriors for most parameters without a significant increase in catastrophic outliers. Furthermore, we demonstrate that small architectures can produce flux residuals that have significant correlations, which can create dangerous systematic errors in colors. Finally, we show that the distributions chosen for generating training sets can have a large effect on an emulator's ability to accurately fit rare objects. Selecting the optimal architecture and training set for an emulator will minimize the computational requirements for fitting near-future large-scale galaxy surveys. We release our emulators on GitHub (http://github.com/elijahmathews/MathewsEtAl2023).
Three-body interactions can eject stars from the core of a globular cluster, causing them to enter the Galactic halo as extra-tidal stars. While finding extra-tidal stars is imperative for understanding cluster evolution, connecting isolated extra-tidal field stars back to their birth cluster is extremely difficult. In this work, we present a new methodology consisting of high-dimensional data analysis and a particle spray code to identify extra-tidal stars of any Galactic globular cluster using M3 as a case study. Using the t-Stochastic Neighbour Embedding and Uniform Manifold Approximation and Projection machine learning dimensionality reduction algorithms, we first identify a set of 103 extra-tidal candidates in the APOGEE DR17 data catalogue with chemical abundances similar to M3 stars. To confirm each candidate’s extra-tidal nature, we introduce corespray – a new python-based three-body particle spray code that simulates extra-tidal stars for any Galactic globular cluster. Using Gaia EDR3 proper motions and APOGEE DR17 radial velocities, we apply multivariate Gaussian modelling and an extreme deconvolution to identify the extra-tidal candidates that are more likely to be associated with a distribution of corespray-simulated M3 extra-tidal stars than the field. Through these methods, we identify 10 new high-probability extra-tidal stars produced via three-body interactions in M3. We also explore whether any of our extra-tidal candidates are consistent with being ejected from M3 through different dynamical processes. Future applications of corespray will yield better understandings of core dynamics, star formation histories, and binary fractions in globular clusters.
In the last two decades, Bayesian inference has become commonplace in astronomy. At the same time, the choice of algorithms, terminology, notation, and interpretation of Bayesian inference varies from one sub-field of astronomy to the next, which can lead to confusion to both those learning and those familiar with Bayesian statistics. Moreover, the choice varies between the astronomy and statistics literature, too. In this paper, our goal is two-fold: (1) provide a reference that consolidates and clarifies terminology and notation across disciplines, and (2) outline practical guidance for Bayesian inference in astronomy. Highlighting both the astronomy and statistics literature, we cover topics such as notation, specification of the likelihood and prior distributions, inference using the posterior distribution, and posterior predictive checking. It is not our intention to introduce the entire field of Bayesian data analysis -- rather, we present a series of useful practices for astronomers who already have an understanding of the Bayesian "nuts and bolts" and wish to increase their expertise and extend their knowledge. Moreover, as the field of astrostatistics and astroinformatics continues to grow, we hope this paper will serve as both a helpful reference and as a jumping off point for deeper dives into the statistics and astrostatistics literature.
A wide-field near-infrared survey of the Galactic disk and bulge/bar(s) is supported by a large representation of the community of Galactic astronomers. The combination of sensitivity, angular resolution and large field of view make Roman uniquely able to study the crowded and highly extincted lines of sight in the Galactic plane. A ~1000 deg2 survey of the bulge and inner Galactic disk would yield an impressive dataset of ~120 billion sources and map the structure of our Galaxy. The effort would foster subsequent expansions in numerous dimensions (spatial, depth, wavelengths, epochs). Importantly, the survey would benefit from early defintion by the community, namely because the Galactic disk is a complex environment, and different science goals will require trade offs.
In previous work, we identified a population of 38 cool and luminous variable stars in the Magellanic Clouds and examined 11 in detail in order to classify them as either Thorne-Żytkow objects (TŻOs; red supergiants with a neutron star cores) or super-asymptotic giant branch (sAGB) stars (the most massive stars that will not undergo core collapse). This population includes HV 2112, a peculiar star previously considered in other works to be either a TŻO or high-mass asymptotic giant branch (AGB) star. Here we continue this investigation, using the kinematic and radio environments and local star formation history of these stars to place constraints on the age of the progenitor systems and the presence of past supernovae. These stars are not associated with regions of recent star formation, and we find no evidence of past supernovae at their locations. Finally, we also assess the presence of heavy elements and lithium in their spectra compared to red supergiants. We find strong absorption in Li and s-process elements compared to RSGs in most of the sample, consistent with sAGB nucleosynthesis, while HV 2112 shows additional strong lines associated with TŻO nucleosynthesis. Coupled with our previous mass estimates, the results are consistent with the stars being massive (~4-6.5 M ☉) or sAGB (~6.5-12 M ☉) stars in the thermally pulsing phase, providing crucial observations of the transition between low- and high-mass stellar populations. HV 2112 is more ambiguous; it could either be a maximally massive sAGB star, or a TŻO if the minimum mass for stability extends down to ≲13 M ☉.
The Nancy Grace Roman Space Telescope is capable of delivering an unprecedented all-sky, high-spatial resolution, multi-epoch infrared map to the astronomical community. This opportunity arises in the midst of numerous ground- and space-based surveys that will provide extensive spectroscopy and imaging together covering the entire sky (such as Rubin/LSST, Euclid, UNIONS, SPHEREx, DESI, SDSS-V, GALAH, 4MOST, WEAVE, MOONS, PFS, UVEX, NEO Surveyor, etc.). Roman can uniquely provide uniform high-spatial-resolution (~0.1 arcsec) imaging over the entire sky, vastly expanding the science reach and precision of all of these near-term and future surveys. This imaging will not only enhance other surveys, but also facilitate completely new science. By imaging the full sky over two epochs, Roman can measure the proper motions for stars across the entire Milky Way, probing 100 times fainter than Gaia out to the very edge of the Galaxy. Here, we propose NANCY: a completely public, all-sky survey that will create a high-value legacy dataset benefiting innumerable ongoing and forthcoming studies of the universe. NANCY is a pure expression of Roman's potential: it images the entire sky, at high spatial resolution, in a broad infrared bandpass that collects as many photons as possible. The majority of all ongoing astronomical surveys would benefit from incorporating observations of NANCY into their analyses, whether these surveys focus on nearby stars, the Milky Way, near-field cosmology, or the broader universe.
Galaxy stellar mass is known to be monotonically related to the size of the galaxy's globular cluster (GC) population for Milky Way sized and larger galaxies. However, the relation becomes ambiguous for dwarf galaxies, where there is some evidence for a downturn in GC population size at low galaxy masses. Smaller dwarfs are increasingly likely to have no GCs, and these zeros cannot be easily incorporated into linear models. We introduce the Hierarchical Errors-in-variables ERrors-in-variables BAyesian Lognormal hurdle (HERBAL) model to represent the relationship between dwarf galaxies and their GC populations, and apply it to the sample of Local Group galaxies, where the luminosity range coverage is maximal. This bimodal model accurately represents the two populations of dwarf galaxies: those that have GCs and those that do not. Our model thoroughly accounts for all uncertainties, including measurement uncertainty, uncertainty in luminosity to stellar mass conversions, and intrinsic scatter. The hierarchical nature of our Bayesian model also allows us to estimate galaxy masses and individual mass-to-light ratios from luminosity data within the model. We find that 50% of galaxies are expected to host GC populations at a stellar mass of {log}₁₀({M}* )=6.996 , and that the expected mass of GC populations remains linear down to the smallest galaxies. Our hierarchical model recovers an accurate estimate of the Milky Way stellar mass. Under our assumed error model, we find a nonzero intrinsic scatter of {0.59}-0.21+0.3 (95% credible interval) that should be accounted for in future models.
We present zephyr, a novel method that integrates cutting-edge normalizing flow techniques into a mixture density estimation framework, enabling the effective use of heterogeneous training data for photometric redshift inference. Compared to previous methods, zephyr demonstrates enhanced robustness for both point estimation and distribution reconstruction by leveraging normalizing flows for density estimation and incorporating careful uncertainty quantification. Moreover, zephyr offers unique interpretability by explicitly disentangling contributions from multi-source training data, which can facilitate future weak lensing analysis by providing an additional quality assessment. As probabilistic generative deep learning techniques gain increasing prominence in astronomy, zephyr should become an inspiration for handling heterogeneous training data while remaining interpretable and robustly accounting for observational uncertainties.
Understanding the entire history of the ionization state of the intergalactic medium (IGM) is at the frontier of astrophysics and cosmology. A promising method to achieve this is by extracting the damping wing signal from the neutral IGM. As hundreds of redshift z>6 quasars are observed, we anticipate determining the detailed time evolution of the ionization fraction with unprecedented fidelity. However, traditional approaches to parameter inference are not sufficiently accurate. We assess the performance of a simulation-based inference (SBI) method to infer the neutral fraction of the universe from quasar spectra. The SBI method adeptly exploits the shape information of the damping wing, enabling precise estimations of the neutral fraction <x_HI>_v and the wing position w_p. Importantly, the SBI framework successfully breaks the degeneracy between these two parameters, offering unbiased estimates of both. This makes the SBI superior to the traditional method using a pseudo-likelihood function. We anticipate that SBI will be essential to determine robustly the ionization history of the Universe through joint inference from the hundreds of high-z spectra we will observe.
Stellar ages are critical building blocks of evolutionary models, but challenging to measure for low mass main sequence stars. An unexplored solution in this regime is the application of probabilistic machine learning methods to gyrochronology, a stellar dating technique that is uniquely well suited for these stars. While accurate analytical gyrochronological models have proven challenging to develop, here we apply conditional normalizing flows to photometric data from open star clusters, and demonstrate that a data-driven approach can constrain gyrochronological ages with a precision comparable to other standard techniques. We evaluate the flow results in the context of a Bayesian framework, and show that our inferred ages recover literature values well. This work demonstrates the potential of a probabilistic data-driven solution to widen the applicability of gyrochronological stellar dating.
The recent release of 220+ million BP/RP spectra in Gaia DR3 presents an opportunity to apply deep learning models to an unprecedented number of stellar spectra, at extremely low-resolution. The BP/RP dataset is so massive that no previous spectroscopic survey can provide enough stellar labels to cover the BP/RP parameter space. We present an unsupervised, deep, generative model for BP/RP spectra: a scatter variational auto-encoder. We design a non-traditional variational auto-encoder which is capable of modeling both (i) BP/RP coefficients and (ii) intrinsic scatter. Our model learns a latent space from which to generate BP/RP spectra (scatter) directly from the data itself without requiring any stellar labels. We demonstrate that our model accurately reproduces BP/RP spectra in regions of parameter space where supervised learning fails or cannot be implemented.
For decades we have known that the Sun lies within the Local Bubble, a cavity of low-density, high-temperature plasma surrounded by a shell of cold, neutral gas and dust, –. However, the precise shape and extent of this shell,, the impetus and timescale for its formation,, and its relationship to nearby star formation have remained uncertain, largely due to low-resolution models of the local interstellar medium. Here we report an analysis of the three-dimensional positions, shapes and motions of dense gas and young stars within 200 pc of the Sun, using new spatial, – and dynamical constraints. We find that nearly all of the star-forming complexes in the solar vicinity lie on the surface of the Local Bubble and that their young stars show outward expansion mainly perpendicular to the bubble’s surface. Tracebacks of these young stars’ motions support a picture in which the origin of the Local Bubble was a burst of …
We investigate the stellar populations for a sample of 161 massive, mainly quiescent galaxies at <z obs> = 0.8 with deep Keck/DEIMOS rest-frame optical spectroscopy (HALO7D survey). With the fully Bayesian framework Prospector, we simultaneously fit the spectroscopic and photometric data with an advanced physical model (including nonparametric star formation histories, emission lines, variable dust attenuation law, and dust and active galactic nucleus emission), together with an uncertainty and outlier model. We show that both spectroscopy and photometry are needed to break the dust-age-metallicity degeneracy. We find a large diversity of star formation histories: although the most massive (M ⋆ > 2 × 1011 M ☉) galaxies formed the earliest (formation redshift of z f ≍ 5-10 with a short star formation timescale of τ SF ≲ 1 Gyr), lower-mass galaxies have a wide range of formation redshifts, leading to only a weak trend of z f with M ⋆. Interestingly, several low-mass galaxies have formation redshifts of z f ≍ 5-8. Star-forming galaxies evolve about the star-forming main sequence, crossing the ridgeline several times in their past. Quiescent galaxies show a wide range and continuous distribution of quenching timescales (τ quench ≍ 0-5 Gyr) with a median of ⟨ {τ }quench⟩ ={1.0}-0.9+0.8 Gyr and of quenching epochs of z quench ≍ 0.8-5.0 ( ⟨ {z}quench⟩ ={1.3}-0.4+0.7 ). This large diversity of quenching timescales and epochs points toward a combination of internal and external quenching mechanisms. In our sample, rejuvenation and "late bloomers" are uncommon. In summary, our analysis supports the "grow-and-quench" framework and is consistent with a wide and continuously populated diversity of quenching timescales.
We use the panchromatic spectral energy distribution (SED)-fitting code Prospector to measure the galaxy logM*-logSFR relationship (the star-forming sequence) across 0.2 < z < 3.0 using the COSMOS-2015 and 3D-HST UV-IR photometric catalogs. We demonstrate that the chosen method of identifying star-forming galaxies introduces a systematic uncertainty in the inferred normalization and width of the star-forming sequence, peaking for massive galaxies at ~0.5 and ~0.2 dex, respectively. To avoid this systematic, we instead parameterize the density of the full galaxy population in the logM*-logSFR-redshift plane using a flexible neural network known as a normalizing flow. The resulting star-forming sequence has a low-mass slope near unity and a much flatter slope at higher masses, with a normalization 0.2-0.5 dex lower than typical inferences in the literature. We show this difference is due to the sophistication of the Prospector stellar populations modeling: the nonparametric star formation histories naturally produce higher masses while the combination of individualized metallicity, dust, and star formation history constraints produce lower star formation rates (SFRs) than typical UV+IR formulae. We introduce a simple formalism to understand the difference between SFRs inferred from SED fitting and standard template-based approaches such as UV+IR SFRs. Finally, we demonstrate the inferred star-forming sequence is consistent with predictions from theoretical models of galaxy formation, resolving a long-standing ~ 0.2-0.5 dex offset with observations at 0.5 < z < 3. The fully trained normalizing flow including a nonparametric description of ρ (log{M}* ,logSFR,z) is available online 20 20 https://github.com/jrleja/sfs_leja_trained_flow to facilitate straightforward comparisons with future work.
We review Skilling's nested sampling (NS) algorithm for Bayesian inference and more broadly multi-dimensional integration. After recapitulating the principles of NS, we survey developments in implementing efficient NS algorithms in practice in high-dimensions, including methods for sampling from the so-called constrained prior. We outline the ways in which NS may be applied and describe the application of NS in three scientific fields in which the algorithm has proved to be useful: cosmology, gravitational-wave astronomy, and materials science. We close by making recommendations for best practice when using NS and by summarizing potential limitations and optimizations of NS.
We use chemistry ([alpha/Fe] and [Fe/H]), main sequence turnoff ages, and kinematics determined from H3 Survey spectroscopy and Gaia astrometry to identify the birth of the Galactic disk. We separate in-situ and accreted stars on the basis of angular momenta and eccentricities. The sequence of high-alpha in-situ stars persists down to at least [Fe/H]=-2.5 and shows unexpected non-monotonic behavior: with increasing metallicity the population first declines in [alpha/Fe], then increases over the range -1.3<[Fe/H]<-0.7, and then declines again at higher metallicities. The number of stars in the in-situ population rapidly increases above [Fe/H]=-1. The average kinematics of these stars are hot and independent of metallicity at [Fe/H]<-1 and then become increasingly cold and disk-like at higher metallicities. The ages of the in-situ, high-alpha stars are uniformly very old (13 Gyr) at [Fe/H]<-1.3, and span a wider range (8-12 Gyr) at higher metallicities. Interpreting the chemistry with a simple chemical evolution model suggests that the non-monotonic behavior is due to a significant increase in star formation efficiency, which began 13 Gyr ago. These results support a picture in which the first 1 Gyr of the Galaxy was characterized by a "simmering phase" in which the star formation efficiency was low and the kinematics had substantial disorder with some net rotation. The disk then underwent a dramatic transformation to a "boiling phase", in which the star formation efficiency increased substantially, the kinematics became disk-like, and the number of stars formed increased tenfold. We interpret this transformation as the birth of the Galactic disk at z~4. The physical origin of this transformation is unclear and does not seem to be reproduced in current galaxy formation models.
Modern Galactic surveys have revealed an ancient merger that dominates the stellar halo of our galaxy (Gaia─Sausage─Enceladus, GSE). Using chemical abundances and kinematics from the H3 Survey, we identify 5559 halo stars from this merger in the radial range r Gal = 6─60kpc. We forward model the full selection function of H3 to infer the density profile of this accreted component of the stellar halo. We consider a general ellipsoid with principal axes allowed to rotate with respect to the galactocentric axes, coupled with a multiply broken power law. The best-fit model is a triaxial ellipsoid (axes ratios 10:8:7) tilted 25° above the Galactic plane toward the Sun and a doubly broken power law with breaking radii at 12 kpc and 28 kpc. The doubly broken power law resolves a long-standing dichotomy in literature values of the halo breaking radius, being at either ∼15 kpc or ∼30 kpc assuming a singly broken power law. N-body simulations suggest that the breaking radii are connected to apocenter pile-ups of stellar orbits, and so the observed double-break provides new insight into the initial conditions and evolution of the GSE merger. Furthermore, the tilt and triaxiality of the stellar halo could imply that a fraction of the underlying dark matter halo is also tilted and triaxial. This has important implications for dynamical mass modeling of the galaxy as well as direct dark matter detection experiments.
The astrophysical origins of r-process elements remain elusive. Neutron star mergers (NSMs) and special classes of core-collapse supernovae (rCCSNe) are leading candidates. Due to these channels' distinct characteristic timescales (rCCSNe: prompt, NSMs: delayed), measuring r-process enrichment in galaxies of similar mass but differing star formation durations might prove informative. Two recently discovered disrupted dwarfs in the Milky Way's stellar halo, Kraken and Gaia-Sausage Enceladus (GSE), afford precisely this opportunity: Both have M ⋆ ≍ 108 M ☉ but differing star formation durations of ≍2 Gyr and ≍3.6 Gyr. Here we present R ≍ 50,000 Magellan/MIKE spectroscopy for 31 stars from these systems, detecting the r-process element Eu in all stars. Stars from both systems have similar [Mg/H] ≍ -1, but Kraken has a median [Eu/Mg] ≍ -0.1 while GSE has an elevated [Eu/Mg] ≍ 0.2. With simple models, we argue NSM enrichment must be delayed by 500-1000 Myr to produce this difference. rCCSNe must also contribute, especially at early epochs, otherwise stars formed during the delay period would be Eu free. In this picture, rCCSNe account for ≍50% of the Eu in Kraken, ≍25% in GSE, and ≍15% in dwarfs with extended star formation durations like Sagittarius. The inferred delay time for NSM enrichment is 10×-100× longer than merger delay times from stellar population synthesis-this is not necessarily surprising because the enrichment delay includes time taken for NSM ejecta to be incorporated into subsequent generations of stars. For example, this may be due to natal kicks that result in r-enriched material deposited far from star-forming gas, which then takes ≍108-109 yr to cool in these galaxies.
We present high-fidelity cosmology results from a blinded joint analysis of galaxy-galaxy weak lensing (ΔΣ) and projected galaxy clustering ({w}p) measured from the Hyper Suprime-Cam Year-1 (HSC-Y1) data and spectroscopic Sloan Digital Sky Survey (SDSS) galaxy catalogs in the redshift range 0.15<z<0.7. We define luminosity-limited samples of SDSS galaxies to serve as the tracers of {w}p in three spectroscopic redshift bins, and as the lens samples for ΔΣ. For the ΔΣ measurements, we select a single sample of 4\ifmmode×\else\texttimes\fi{}{10}⁶ source galaxies over 140 {deg}² from HSC-Y1 with photometric redshifts (photo z) greater than 0.75, enabling a better handle of photo-z errors by comparing the ΔΣ amplitudes for the three lens redshift bins. The deep, high-quality HSC-Y1 data enable significant detections of the ΔΣ signals, with integrated signal-to-noise ratio S/N∼15 in the range 3\leR/[{h}⁻¹ Mpc]≤30 for the three lens samples, despite the small area coverage. For cosmological parameter inference, we use an input galaxy-halo connection model built on the dark emulator package (which uses an ensemble set of high-resolution N-body simulations and enables fast, accurate computation of the clustering observables) with a halo occupation distribution that includes nuisance parameters to marginalize over modeling uncertainties. We model the ΔΣ and {w}p measurements on scales from R≃3 and 2 {h}⁻¹ Mpc, respectively, up to 30 {h}⁻¹ Mpc (therefore excluding the baryon acoustic oscillations information) assuming a flat \LambdaCDM cosmology, marginalizing over about 20 nuisance parameters and demonstrating the robustness of our results to them. With various tests using mock catalogs described in Miyatake et al. [preceding paper, Phys. Rev. D 106, 083519 (2022)], we show that any bias in the clustering amplitude {S}₈≡{σ}₈({Ω}m/0.3{)}0.5 due to uncertainties in the galaxy-halo connection is less than ∼50% of the statistical uncertainty of {S}₈, unless the assembly biaseffect is unexpectedly large. Our best-fit models have {S}₈=0.79{5}-0.042+0.049 (mode and 68% credible interval) for the flat \LambdaCDM model; we find tighter constraints on the quantity {S}₈(α=0.17)≡{σ}₈({Ω}m/0.3{)}0.17=0.74{5}-0.031+0.039.
The mass of the Milky Way is a critical quantity that, despite decades of research, remains uncertain within a factor of two. Until recently, most studies have used dynamical tracers in the inner regions of the halo, relying on extrapolations to estimate the mass of the Milky Way. In this paper, we extend the hierarchical Bayesian model applied in Eadie & Juri to study the mass distribution of the Milky Way halo; the new model allows for the use of all available 6D phase-space measurements. We use kinematic data of halo stars out to 142 kpc, obtained from the H3 survey and Gaia EDR3, to infer the mass of the Galaxy. Inference is carried out with the No-U-Turn sampler, a fast and scalable extension of Hamiltonian Monte Carlo. We report a median mass enclosed within 100 kpc of M(< 100 kpc)={0.69}-0.04+0.05× {10}¹² {M}⊙ (68% Bayesian credible interval), or a virial mass of {M}₂₀₀=M(< {216.2}-7.5+7.5 kpc)={1.08}-0.11+0.12× {10}¹² {M}⊙ , in good agreement with other recent estimates. We analyze our results using posterior predictive checks and find limitations in the model's ability to describe the data. In particular, we find sensitivity with respect to substructure in the halo, which limits the precision of our mass estimates to ~15%.
The Milky Way's satellite galaxies ("surviving dwarfs") have been studied for decades as unique probes of chemical evolution in the low-mass regime. Here we extend such studies to the "disrupted dwarfs", whose debris constitutes the stellar halo. We present abundances ([Fe/H], [α/Fe]) and stellar masses for nine disrupted dwarfs with M⋆≈10⁶-10⁹M⊙ from the H3 Survey (Sagittarius, Gaia-Sausage-Enceladus, Helmi Streams, Sequoia, Wukong/LMS-1, Cetus, Thamnos, I'itoi, Orphan/Chenab). The surviving and disrupted dwarfs are chemically distinct: at fixed mass, the disrupted dwarfs are systematically metal-poor and α-enhanced. The disrupted dwarfs define a mass-metallicity relation (MZR) with a similar slope as the z=0 MZR followed by the surviving dwarfs, but offset to lower metallicities by Δ[Fe/H]≈0.3-0.4 dex. Dwarfs with larger offsets from the z=0 MZR are more α-enhanced. In simulations as well as observations, galaxies with higher Δ[Fe/H] formed at higher redshifts -- exploiting this, we infer the disrupted dwarfs have typical star-formation truncation redshifts of ztrunc{∼}1-2. We compare the chemically inferred ztrunc with dynamically inferred accretion redshifts and find almost all dwarfs are quenched only after accretion. The differences between disrupted and surviving dwarfs are likely because the disrupted dwarfs assembled their mass rapidly, at higher redshifts, and within denser dark matter halos that formed closer to the Galaxy. Our results place novel archaeological constraints on low-mass galaxies inaccessible to direct high-z studies: (i) the redshift evolution of the MZR along parallel tracks but offset to lower metallicities extends to M⋆≈10⁶-10⁹M⊙; (ii) galaxies at z≈2-3 are α-enhanced with [α/Fe]≈0.4.
Recent observations of the stellar halo have uncovered the debris of an ancient merger, Gaia–Sausage–Enceladus (GSE), estimated to have occurred ≳8 Gyr ago. Follow-up studies have associated GSE with a large-scale tilt in the stellar halo that links two well-known stellar overdensities in diagonally opposing octants of the Galaxy (the Hercules–Aquila Cloud and Virgo Overdensity; HAC and VOD). In this paper, we study the plausibility of such unmixed merger debris persisting over several gigayears in the Galactic halo. We employ the simulated stellar halo from Naidu et al., which reproduces several key properties of the merger remnant, including the large-scale tilt. By integrating the orbits of these simulated stellar halo particles, we show that adoption of a spherical halo potential results in rapid phase mixing of the asymmetry. However, adopting a tilted halo potential preserves the initial asymmetry in the stellar halo for many gigayears. The asymmetry is preserved even when a realistic growing disk is added to the potential. These results suggest that HAC and VOD are long-lived structures that are associated with GSE and that the dark matter halo of the Galaxy is tilted with respect to the disk and aligned in the direction of HAC–VOD. Such halo–disk misalignment is common in modern cosmological simulations. Lastly, we study the relationship between the local and global stellar halo in light of a tilted global halo comprised of highly radial orbits. We find that the local halo offers a dynamically biased view of the global halo due to its displacement from the Galactic center.
Using the weak gravitational lensing data from the Hyper Suprime-Cam Subaru Strategic Program (HSC survey), we study the potential of different stellar mass estimates in tracing halo mass. We consider galaxies with log10(M⋆/M☉) > 11.5 at 0.2 < z < 0.5 with carefully measured light profiles, and clusters from the redMaPPer and CAMIRA richness-based algorithms. We devise a method (the 'Top-N test') to evaluate the scatter in the halo mass-observable relation for different tracers, and to inter-compare halo mass proxies in four number density bins using stacked galaxy-galaxy lensing profiles. This test reveals three key findings. Stellar masses based on CModel photometry and aperture luminosity within R <30 kpc are poor proxies of halo mass. In contrast, the stellar mass of the outer envelope is an excellent halo mass proxy. The stellar mass within R = [50, 100] kpc, M⋆, [50, 100], has performance comparable to the state-of-the-art richness-based cluster finders at log10Mvir ≳ 14.0 and could be a better halo mass tracer at lower halo masses. Finally, using N-body simulations, we find that the lensing profiles of massive haloes selected by M⋆, [50, 100] are consistent with the expectation for a sample without projection or mis-centring effects. Richness-selected clusters, on the other hand, display an excess at R ~ 1 Mpc in their lensing profiles, which may suggest a more significant impact from selection biases. These results suggest that M⋆-based tracers have distinct advantages in identifying massive haloes, which could open up new avenues for cluster cosmology. The codes and data used in this work can be found here:
We present cosmological parameter constraints from a blinded joint analysis of galaxy-galaxy weak lensing, ΔΣ(R), and the projected correlation function, {w}p(R), measured from the first-year HSC (HSC-Y1) data and SDSS spectroscopic galaxies over 0.15<z<0.7. We use luminosity-limited samples as lens samples for ΔΣ and as large-scale structure tracers for {w}p in three redshift bins, and use the HSC-Y1 galaxy catalog to define a secure sample of source galaxies at {z}ph>0.75 for the ΔΣ measurements, selected based on their photometric redshifts. As a theoretical template, we use the “minimal bias” model for the cosmological clustering observables for the flat \LambdaCDM cosmological model. We compare the model predictions with the measurements in each redshift bin on large scales, R>12 and 8{h}⁻¹ Mpc for ΔΣ(R) and {w}p(R), respectively, where the perturbation-theory-inspired model is valid. As part of our model, we account for the effect of lensing magnification bias on the ΔΣ measurements. When we employ weak priors on cosmological parameters, without cosmic microwave background (CMB) information, we find {S}₈=0.93{6}-0.086+0.092, {σ}₈=0.8{5}-0.11+0.16, and {Ω}m=0.28{3}-0.035+0.12 (mode and 68% credible interval) for the flat \LambdaCDM model. Although the central value of {S}₈ appears to be larger than those inferred from other cosmological experiments, we find that the difference is consistent with expected differences due to sample variance, and our results are consistent with the other results to within the statistical uncertainties. When combined with the Planck 2018 likelihood for the primary CMB anisotropy information (TT,TE,EE+lowE), we find {S}₈=0.81{7}-0.021+0.022, {σ}₈=0.89{2}-0.056+0.051, {Ω}m=0.24{6}-0.035+0.045, and the equation-of-state parameter of dark energy, {w}de=-1.2{8}-0.19+0.20 for the flat wCDM model, which is consistent with the flat \LambdaCDM model to within the error bars.
Lensing without borders is a cross-survey collaboration created to assess the consistency of galaxy-galaxy lensing signals (∆Σ) across different data sets and to carry out end-to-end tests of systematic errors. We perform a blind comparison of the amplitude of ∆Σ using lens samples from BOSS and six independent lensing surveys. We find good agreement between empirically estimated and reported systematic errors which agree to better than 2.3σ in four lens bins and three radial ranges. For lenses with zL > 0.43 and considering statistical errors, we detect a 3-4σ correlation between lensing amplitude and survey depth. This correlation could arise from the increasing impact at higher redshift of unrecognized galaxy blends on shear calibration and imperfections in photometric redshift calibration. At zL > 0.54, amplitudes may additionally correlate with foreground stellar density. The amplitude of these trends is within survey-defined systematic error budgets that are designed to include known shear and redshift calibration uncertainty. Using a fully empirical and conservative method, we do not find evidence for large unknown systematics. Systematic errors greater than 15 per cent (25 per cent) ruled out in three lens bins at 68 per cent (95 per cent) confidence at z < 0.54. Differences with respect to predictions based on clustering are observed to be at the 20-30 per cent level. Our results therefore suggest that lensing systematics alone are unlikely to fully explain the 'lensing is low' effect at z < 0.54. This analysis demonstrates the power of cross-survey comparisons and provides a promising path for identifying and reducing systematics in future lensing analyses.
We present an analysis of the kinematics of the Radcliffe Wave, a 2.7 kpc long sinusoidal band of molecular clouds in the solar neighborhood recently detected via 3D dust mapping. With Gaia DR2 astrometry and spectroscopy, we analyze the 3D space velocities of ∼1500 young stars along the Radcliffe Wave in action-angle space, using the motion of the wave’s newly born stars as a proxy for its gas motion. We find that the vertical angle of young stars—corresponding to their orbital phase perpendicular to the Galactic plane—varies significantly as a function of position along the structure, in a pattern potentially consistent with a wavelike oscillation. This kind of oscillation is not seen in a control sample of older stars from Gaia occupying the same volume, disfavoring formation channels caused by long-lived physical processes. We use a “wavy midplane” model to try to account for the trend in vertical angles seen in young stars, and find that while the best-fit parameters for the wave’s spatial period and amplitude are qualitatively consistent with the existing morphology defined by 3D dust, there is no evidence for additional velocity structure. These results support more recent and/or transitory processes in the formation of the Radcliffe Wave, which would primarily affect the motion of the wave’s gaseous material. Comparisons of our results with new and upcoming simulations, in conjunction with new stellar radial velocity measurements in Gaia DR3, should allow us to further discriminate between various competing hypotheses.
Due to the different environments in the Milky Way's disc and halo, comparing wide binaries in the disc and halo is key to understanding wide binary formation and evolution. By using Gaia Early Data Release 3, we search for resolved wide binary companions in the H3 survey, a spectroscopic survey that has compiled ~150 000 spectra for thick-disc and halo stars to date. We identify 800 high-confidence (a contamination rate of 4 per cent) wide binaries and two resolved triples, with binary separations mostly between 103 and 105 au and a lowest [Fe/H] of -2.7. Based on their Galactic kinematics, 33 of them are halo wide binaries, and most of those are associated with the accreted Gaia-Sausage-Enceladus galaxy. The wide binary fraction in the thick disc decreases toward the low metallicity end, consistent with the previous findings for the thin disc. Our key finding is that the halo wide binary fraction is consistent with the thick-disc stars at a fixed [Fe/H]. There is no significant dependence of the wide binary fraction on the α-captured abundance. Therefore, the wide binary fraction is mainly determined by the iron abundance, not their disc or halo origin nor the α-captured abundance. Our results suggest that the formation environments play a major role for the wide binary fraction, instead of other processes like radial migration that only apply to disc stars.
We report the discovery of Specter, a disrupted ultrafaint dwarf galaxy revealed by the H3 Spectroscopic Survey. We detected this structure via a pair of comoving metal-poor stars at a distance of 12.5 kpc, and further characterized it with Gaia astrometry and follow-up spectroscopy. Specter is a 25° × 1° stream of stars that is entirely invisible until strict kinematic cuts are applied to remove the Galactic foreground. The spectroscopic members suggest a stellar age τ ≳ 12 Gyr and a mean metallicity <inline-formula> <mml:math><mml:mo><</mml:mo><mml:mo>[</mml:mo><mml:mi>Fe</mml:mi><mml:mrow><mml:mo>/</mml:mo></mml:mrow><mml:mi>H</mml:mi><mml:mo>]</mml:mo><mml:mo>></mml:mo><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:msubsup><mml:mrow><mml:mn>1.84</mml:mn></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>0.18</mml:mn></mml:mrow><mml:mrow><mml:mo>+</mml:mo><mml:mn>0.16</mml:mn></mml:mrow></mml:msubsup></mml:math> </inline-formula>, with a significant intrinsic metallicity dispersion <inline-formula> <mml:math><mml:msub><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mi>Fe</mml:mi><mml:mrow><mml:mo>/</mml:mo></mml:mrow><mml:mi>H</mml:mi><mml:mo>]</mml:mo></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msubsup><mml:mrow><mml:mn>0.37</mml:mn></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>0.13</mml:mn></mml:mrow><mml:mrow><mml:mo>+</mml:mo><mml:mn>0.21</mml:mn></mml:mrow></mml:msubsup></mml:math> </inline-formula>. We therefore argue that Specter is the disrupted remnant of an ancient dwarf galaxy. With an integrated luminosity M V ≈ -2.6, Specter is by far the least-luminous dwarf galaxy stream known. We estimate that dozens of similar streams are lurking below the detection threshold of current search techniques, and conclude that spectroscopic surveys offer a novel means to identify extremely low surface brightness structures.
We analyze the velocity anisotropy of stars in real and energy space for a sample of Milky Way-like galaxies in the TNG50 simulation. We employ different selection criteria, including spatial, kinematic, and metallicity cuts, and make three halo classes ( A - C ) that show mild-to-strong sensitivity to different selections. The above classes cover 48%, 16%, and 36% of the halos, respectively. We analyze the β radial profiles and divide them into either monotonically increasing radial profiles or ones with peaks and troughs. We demonstrate that halos with monotonically increasing β profiles are mostly from class A , while those with peaks/troughs are part of classes B and C . This means that care must be taken, as the observationally reported peaks/troughs might be a consequence of different selection criteria. We infer the anisotropy parameter β energy space and compare that against the β radial profile. It is seen than 65% of halos with very mild sensitivity to different selections in real space are those for which the β radial and energy profiles are closely related. Consequently, we propose that comparing the β radial and energy profiles might be a novel way to examine the sensitivity to different selection criteria and thus examining the robustness of the anisotropy parameter in tracing stellar kinematics. We compare simulated β radial profiles against various observations and demonstrate that, in most cases, the model diversity is comparable with the error bars from different observations, meaning that the TNG50 models are in good overall agreement with observations.
Upcoming astronomical surveys will observe billions of galaxies across cosmic time, providing a unique opportunity to map the many pathways of galaxy assembly to an incredibly high resolution. However, the huge amount of data also poses an immediate computational challenge: current tools for inferring parameters from the light of galaxies take ≳ 10 hours per fit. This is prohibitively expensive. Simulation-based Inference (SBI) is a promising solution. However, it requires simulated data with identical characteristics to the observed data, whereas real astronomical surveys are often highly heterogeneous, with missing observations and variable uncertainties determined by sky and telescope conditions. Here we present a Monte Carlo technique for treating out-of-distribution measurement errors and missing data using standard SBI tools. We show that out-of-distribution measurement errors can be approximated by using standard SBI evaluations, and that missing data can be marginalized over using SBI evaluations over nearby data realizations in the training set. While these techniques slow the inference process from ∼ 1 sec to ∼ 1.5 min per object, this is still significantly faster than standard approaches while also dramatically expanding the applicability of SBI. This expanded regime has broad implications for future applications to astronomical surveys.
In Fig. 5a of this article, three arrows and an area of red shading were missing in the original version. This error has been corrected in the HTML and PDF versions of the article.
Refereeing is a crucial component of publishing astronomical research, but few professional astronomers receive formal training on how to effectively referee a manuscript. In this article, we lay out considerations and best practices for referees. This document is intended as a tool for early career researchers to develop a fair, effective, and efficient approach to refereeing.
Inference of the physical properties of stellar populations from observed photometry and spectroscopy is a key goal in the study of galaxy evolution. In recent years, the quality and quantity of the available data have increased, and there have been corresponding efforts to increase the realism of the stellar population models used to interpret these observations. Describing the observed galaxy spectral energy distributions in detail now requires physical models with a large number of highly correlated parameters. These models do not fit easily on grids and necessitate a full exploration of the available parameter space. We present Prospector , a flexible code for inferring stellar population parameters from photometry and spectroscopy spanning UV through IR wavelengths. This code is based on forward modeling the data and Monte Carlo sampling the posterior parameter distribution, enabling complex models and exploration of moderate dimensional parameter spaces. We describe the key ingredients of the code and discuss the general philosophy driving the design of these ingredients. We demonstrate some capabilities of the code on several data sets, including mock and real data.
Several lines of evidence suggest that the Milky Way underwent a major merger at z ~ 2 with the Gaia-Sausage-Enceladus (GSE) galaxy. Here we use H3 Survey data to argue that GSE entered the Galaxy on a retrograde orbit based on a population of highly retrograde stars with chemistry similar to the largely radial GSE debris. We present the first tailored N-body simulations of the merger. From a grid of ≍500 simulations we find that a GSE with M ⋆ = 5 × 108 M ☉, M DM = 2 × 1011 M ☉ best matches the H3 data. This simulation shows that the retrograde stars are stripped from GSE's outer disk early in the merger. Despite being selected purely on angular momenta and radial distributions, this simulation reproduces and explains the following phenomena: (i) the triaxial shape of the inner halo, whose major axis is at ≍35° to the plane and connects GSE's apocenters; (ii) the Hercules-Aquila Cloud and the Virgo Overdensity, which arise due to apocenter pileup; and (iii) the 2 Gyr lag between the quenching of GSE and the truncation of the age distribution of the in situ halo, which tracks the lag between the first and final GSE pericenters. We make the following predictions: (i) the inner halo has a "double-break" density profile with breaks at both ≍15-18 kpc and 30 kpc, coincident with the GSE apocenters; and (ii) the outer halo has retrograde streams awaiting discovery at >30 kpc that contain ≍10% of GSE's stars. The retrograde (radial) GSE debris originates from its outer (inner) disk-exploiting this trend, we reconstruct the stellar metallicity gradient of GSE (-0.04 ± 0.01 dex {r}₅₀⁻¹ ). These simulations imply that GSE delivered ≍20% of the Milky Way's present-day dark matter and ≍50% of its stellar halo.
We compare the star-forming main sequence (SFMS) of galaxies - both integrated and resolved on 1 kpc scales - between the high-resolution TNG50 simulation of IllustrisTNG and observations from the 3D-HST slitless spectroscopic survey at z ~ 1. Contrasting integrated star formation rates (SFRs), we find that the slope and normalization of the star-forming main sequence in TNG50 are quantitatively consistent with values derived by fitting observations from 3D-HST with the Prospector Bayesian inference framework. The previous offsets of 0.2-1 dex between observed and simulated main-sequence normalizations are resolved when using the updated masses and SFRs from Prospector. The scatter is generically smaller in TNG50 than in 3D-HST for more massive galaxies with M*> 1010 M☉, by ~10-40 per cent, after accounting for observational uncertainties. When comparing resolved star formation, we also find good agreement between TNG50 and 3D-HST: average specific star formation rate (sSFR) radial profiles of galaxies at all masses and radii below, on, and above the SFMS are similar in both normalization and shape. Most noteworthy, massive galaxies with M*> 1010.5 M☉, which have fallen below the SFMS due to ongoing quenching, exhibit a clear central SFR suppression, in both TNG50 and 3D-HST. In contrast, the original Illustris simulation and a variant TNG run without black hole kinetic wind feedback, do not reproduce the central SFR profile suppression seen in data. In TNG, inside-out quenching is due to the supermassive black hole (SMBH) feedback model operating at low accretion rates.
We leverage the 1 pc spatial resolution of the Leike et al. three-dimensional (3D) dust map to characterize the 3D structure of nearby molecular clouds (d ≲ 400 pc). We start by "skeletonizing" the clouds in 3D volume density space to determine their "spines," which we project on the sky to constrain cloud distances with ≍1% uncertainty. For each cloud, we determine an average radial volume density profile around its 3D spine and fit the profiles using Gaussian and Plummer functions. The radial volume density profiles are well described by a two-component Gaussian function, consistent with clouds having broad, lower-density outer envelopes and narrow, higher-density inner layers. The ratio of the outer to inner envelope widths is ≍3:1. We hypothesize that these two components may be tracing a transition between atomic and diffuse molecular gas or between the unstable and cold neutral medium. Plummer-like models can also provide a good fit, with molecular clouds exhibiting shallow power-law wings with density, n, falling off like n-2 at large radii. Using Bayesian model selection, we find that parameterizing the clouds' profiles using a single Gaussian is disfavored. We compare our results with two-dimensional dust extinction maps, finding that the 3D dust recovers the total cloud mass from integrated approaches with fidelity, deviating only at higher levels of extinction (AV ≳ 2-3 mag). The 3D cloud structure described here will enable comparisons with synthetic clouds generated in simulations, offering unprecedented insight into the origins and fates of molecular clouds in the interstellar medium.
The origins of most stellar streams in the Milky Way are unknown. With improved proper motions provided by Gaia EDR3, we show that the orbits of 23 Galactic stellar streams are highly clustered in orbital phase space. Based on their energies and angular momenta, most streams in our sample can plausibly be associated with a specific (disrupted) dwarf galaxy host that brought them into the Milky Way. For eight streams we also identify likely globular cluster progenitors (four of these associations are reported here for the first time). Some of these stream progenitors are surprisingly far apart, displaced from their tidal debris by a few to tens of degrees. We identify stellar streams that appear spatially distinct, but whose similar orbits indicate they likely originate from the same progenitor. If confirmed as physical discontinuities, they will provide strong constraints on the mass loss from the progenitor. The nearly universal ex situ origin of existing stellar streams makes them valuable tracers of galaxy mergers and dynamical friction within the Galactic halo. Their phase-space clustering can be leveraged to construct a precise global map of dark matter in the Milky Way, while their internal structure may hold clues to the small-scale structure of dark matter in their original host galaxies.
Ancient, very metal-poor (VMP) stars offer a window into the earliest epochs of galaxy formation and assembly. We combine data from the H3 Spectroscopic Survey and Gaia to measure metallicities, abundances of α elements, stellar ages, and orbital properties of a sample of 482 VMP ([Fe/H] < −2) stars in order to constrain their origins. This sample is confined to 1 ≲ ∣Z∣ ≲ 3 kpc from the Galactic plane. We find that >70% of VMP stars near the disk are on prograde orbits and this fraction increases toward lower metallicities. This result is unexpected if metal-poor stars are predominantly accreted from many small systems with no preferred orientation, as such a scenario would imply a mostly isotropic distribution. Furthermore, we find there is some evidence for higher fractions of prograde orbits among stars with lower [α/Fe]. Isochrone-based ages for main-sequence turn-off stars reveal that these VMP stars are uniformly old (≈12 Gyr) irrespective of the α abundance and metallicity, suggesting that the metal-poor population was not born from the same well-mixed gas disk. We speculate that the VMP population has a heterogeneous origin, including both in situ formation in the ancient disk and accretion from a satellite with the same direction of rotation as the ancient disk at early times. Our precisely measured ages for these VMP stars on prograde orbits show that the Galaxy has had a relatively quiescent merging history over most of cosmic time, and implies the angular momentum alignment of the Galaxy has been in place for at least 12 Gyr.
We investigate the morphology of the stellar distribution (SD) in a sample of Milky Way–like galaxies in the TNG50 simulation. Using a local in shell iterative method as the main approach, we explicitly show evidence of twisting (in about 52% of halos) and stretching (in 48% of them) in real space. This is matched with the reorientation observed in the eigenvectors of the inertia tensor and gives us a clear picture of having a reoriented SD. We make a comparison between the shape profile of the dark matter (DM) halo and SD and quite remarkably see that their radial profiles are fairly close, especially at small galactocentric radii, where the stellar disk is located. This implies that the DM halo is somewhat aligned with stars in response to the baryonic potential. The level of alignment mostly decreases away from the center. We study the impact of substructures in the orbital circularity parameter. It is demonstrated that in some cases, faraway substructures are counterrotating compared with the central stars and may flip the sign of total angular momentum and thus the orbital circularity parameter. Truncating them above 150 kpc, however, retains the disky structure of the galaxy as per initial selection. Including the impact of substructures in the shape of stars, we explicitly show that their contribution is subdominant. Overlaying our theoretical results on the observational constraints from previous literature, we establish fair agreement.
We developed a data-driven model to map stellar parameters (Teff, logg , and [Fe/{H}] ) accurately and precisely to broadband stellar photometry. This model must, and does, simultaneously constrain the passband-specific dust reddening vector in the Milky Way, R. The model uses a neural network to learn the (de-reddened) absolute magnitude in one band and colors across many bands, given stellar parameters from spectroscopic surveys and parallax constraints from Gaia. To demonstrate the effectiveness of this approach, we train our model on a data set with spectroscopic parameters from LAMOST, APOGEE, and GALAH, Gaia parallaxes, and optical and near-infrared photometry from Gaia, Pan-STARRS 1, Two Micron All Sky Survey and Wide-field Infrared Survey Explorer. Testing the model on these data sets leads to an excellent fit and a precise—and by construction—accurate prediction of the color-magnitude diagrams in many bands. This flexible approach rigorously links spectroscopic and photometric surveys, and also results in an improved, Teff-dependent R. As such, it provides a simple and accurate method for predicting photometry in stellar evolutionary models. Our model will form a basis to infer stellar properties, distances, and dust extinction from photometric data, which should be of great use in 3D mapping of the Milky Way. Our trained model can be obtained at doi:10.5281/zenodo.3902382.
Without a proper accounting of known and unknown systematics and uncertainties, combining information across multiple surveys, wavelengths, and detectors may be risky. Realizing the true potential of multi-messenger and panchromatic astrophysics requires getting data integration right.
We present dynesty, a public, open-source, python package to estimate Bayesian posteriors and evidences (marginal likelihoods) using the dynamic nested sampling methods developed by Higson et al. By adaptively allocating samples based on posterior structure, dynamic nested sampling has the benefits of Markov chain Monte Carlo (MCMC) algorithms that focus exclusively on posterior estimation while retaining nested sampling’s ability to estimate evidences and sample from complex, multimodal distributions. We provide an overview of nested sampling, its extension to dynamic nested sampling, the algorithmic challenges involved, and the various approaches taken to solve them in this and previous work. We then examine dynesty’s performance on a variety of toy problems along with several astronomical applications. We find in particular problems dynesty can provide substantial improvements in sampling efficiency compared to popular MCMC approaches in the astronomical literature. More detailed statistical results related to nested sampling are also included in the appendix.
Accurate distances to local molecular clouds are critical for understanding the star and planet formation process, yet distance measurements are often obtained inhomogeneously on a cloud-by-cloud basis. We have recently developed a method that combines stellar photometric data with Gaia DR2 parallax measurements in a Bayesian framework to infer the distances of nearby dust clouds to a typical accuracy of ∼5%. After refining the technique to target lower latitudes and incorporating deep optical data from DECam in the southern Galactic plane, we have derived a catalog of distances to molecular clouds in Reipurth (2008, Star Formation Handbook, Vols. I and II) which contains a large fraction of the molecular material in the solar neighborhood. Comparison with distances derived from maser parallax measurements towards the same clouds shows our method produces consistent distances with ≲10% scatter for clouds across our entire distance spectrum (150 pc-2.5 kpc). We hope this catalog of homogeneous distances will serve as a baseline for future work. <P />Table A.1 is also available at the CDS via anonymous ftp to <A href="http://cdsarc.u-strasbg.fr/">http://cdsarc.u-strasbg.fr</A> (ftp://130.79.128.5) or via <A href="http://cdsarc.u-strasbg.fr/viz-bin/cat/J/A+A/633/A51">http://cdsarc.u-strasbg.fr/viz-bin/cat/J/A+A/633/A51</A>. It is also available on the Harvard Dataverse at <A href="https://doi.org/10.7910/DVN/07L7YZ">https://doi.org/10.7910/DVN/07L7YZ</A> <P />An interactive 3D version of Fig. 2 is available at <A href="https://www.aanda.org/10.1051/0004-6361/201936145/olm">https://www.aanda.org</A>
For the past 150 years, the prevailing view of the local interstellar medium has been based on a peculiarity known as the Gould Belt, , –, an expanding ring of young stars, gas and dust, tilted about 20 degrees to the Galactic plane. However, the physical relationship between local gas clouds has remained unknown because the accuracy in distance measurements to such clouds is of the same order as, or larger than, their sizes, –. With the advent of large photometric surveys and the astrometric survey, this situation has changed. Here we reveal the three-dimensional structure of all local cloud complexes. We find a narrow and coherent 2.7-kiloparsec arrangement of dense gas in the solar neighbourhood that contains many of the clouds thought to be associated with the Gould Belt. This finding is inconsistent with the notion that these clouds are part of a ring, bringing the Gould Belt model into question. The structure …
There has been a long-standing factor-of-two tension between the observed star formation rate density and the observed stellar mass buildup after z ∼ 2. Recently, we have proposed that sophisticated panchromatic SED models can resolve this tension, as these methods infer systematically higher masses and lower star formation rates than standard approaches. In a series of papers, we now extend this analysis and present a complete, self-consistent census of galaxy formation over 0.2 < z < 3 inferred with the Prospector galaxy SED-fitting code. In this work, Paper I, we present the evolution of the galaxy stellar mass function using new mass measurements of ∼105 galaxies in the 3D-HST and COSMOS-2015 surveys. We employ a new methodology to infer the mass function from the observed stellar masses: instead of fitting independent mass functions in a series of fixed redshift intervals, we construct a continuity model that directly fits for the redshift evolution of the mass function. This approach ensures a smooth picture of galaxy assembly and makes use of the full, non-Gaussian uncertainty contours in our stellar mass inferences. The resulting mass function has higher number densities at a fixed stellar mass than almost any other measurement in the literature, largely owing to the older stellar ages inferred by Prospector. The stellar mass density is ∼50% higher than previous measurements, with the offset peaking at z ∼ 1. The next two papers in this series will present the new measurements of the star-forming main sequence and the cosmic star formation rate density, respectively.
Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-zs at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2 - 2.6 redshift range that the Euclid mission will probe. We designed a challenge using emulated Euclid data drawn from three photometric surveys of the COSMOS field. The data was divided into two samples: one calibration sample for which photometry and redshifts were provided to the participants; and the validation sample, containing only the photometry to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates the sources they consider unfit for use in cosmological analyses. The performance of each method was assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, that is to say sources for which the photo-z deviates by more than 0.15(1 + z) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. We find that no machine-learning method provides good results in the regions of galaxy color-space that are sparsely populated by spectroscopic-redshifts, for example z > 1. However they generally perform better than template-fitting methods at low redshift (z < 0.7), indicating that template-fitting methods do not use all of the information contained in the photometry. We introduce metrics that quantify both photo-z precision and completeness of the samples (post-rejection), since both contribute to the final figure of merit of the science goals of the survey (e.g., cosmic shear from Euclid). Template-fitting methods provide the best results in these metrics, but we show that a combination of template-fitting results and machine-learning results with rejection criteria can outperform any individual method. On this basis, we argue that further work in identifying how to best select between machine-learning and template-fitting approaches for each individual galaxy should be pursued as a priority.
We present MINESweeper, a tool to measure stellar parameters by jointly fitting observed spectra and broadband photometry to model isochrones and spectral libraries. This approach enables the measurement of spectrophotometric distances, in addition to stellar parameters such as Teff, logg, [Fe/H], [α/Fe], and radial velocity. MINESweeper employs a Bayesian framework and can easily incorporate a variety of priors, including Gaia parallaxes. Mock data are fit in order to demonstrate how the precision of derived parameters depends on evolutionary phase and signal-to-noise ratio. We then fit a selection of data in order to validate the model outputs. Fits to a variety of benchmark stars including Procyon, Arcturus, and the Sun result in derived stellar parameters that are in good agreement with the literature. We then fit combined spectra and photometry of stars in the open and globular clusters M92, M13, M3, M107, M71, and M67. Derived distances, [Fe/H], [α/Fe], and logg-Teff relations are in overall good agreement with literature values, although there are trends between metallicity and logg within clusters that point to systematic uncertainties at the ≍0.1 dex level. Finally, we fit a large sample of stars from the H3 Spectroscopic Survey in which high-quality Gaia parallaxes are also available. These stars are fit without the Gaia parallaxes so that the geometric parallaxes can serve as an independent test of the spectrophotometric distances. Comparison between the two reveals good agreement within their formal uncertainties after accounting for the Gaia zero-point uncertainties.
The 100° long thin stellar stream in the Milky Way halo, GD-1, has an ensemble of features that may be due to dynamical interactions. Using high-resolution MMT/Hectochelle spectroscopy we show that a spur of GD-1-like stars outside of the main stream are kinematically and chemically consistent with the main stream. In the spur, as in the main stream, GD-1 has a low intrinsic radial velocity dispersion, σV_r ≲ 1 km s-1, is metal-poor, [Fe/H] ≈ -2.3, and has little intrinsic spread in the [Fe/H] and [α/Fe] abundances, which point to a common globular cluster progenitor. At a fixed location along the stream, the median radial velocity offset between the spur and the main stream is smaller than 0.5 km s-1, comparable to the measurement uncertainty. A flyby of a massive, compact object can change orbits of stars in a stellar stream and produce features like the spur observed in GD-1. In this scenario, the radial velocity of the GD-1 spur relative to the stream constrains the orbit of the perturber and its current on-sky position to ≈5000 deg2. The family of acceptable perturber orbits overlaps the stellar and dark-matter debris of the Sagittarius dwarf galaxy in present-day position and velocity. This suggests that GD-1 may have been perturbed by a globular cluster or an extremely compact dark-matter subhalo formerly associated with Sagittarius.
Using deep images from the Hyper Suprime-Cam (HSC) survey and taking advantage of its unprecedented weak lensing capabilities, we reveal a remarkably tight connection between the stellar mass distribution of massive central galaxies and their host dark matter halo mass. Massive galaxies with more extended stellar mass distributions tend to live in more massive dark matter haloes. We explain this connection with a phenomenological model that assumes, (1) a tight relation between the halo mass and the total stellar content in the halo, (2) that the fraction of in situ and ex situ mass at r <10 kpc depends on halo mass. This model provides an excellent description of the stellar mass functions (SMFs) of total stellar mass (M⋆ max) and stellar mass within inner 10 kpc (M⋆ ¹⁰) and also reproduces the HSC weak lensing signals of massive galaxies with different stellar mass distributions. The best-fitting model shows that halo mass varies significantly at fixed total stellar mass (as much as 0.4 dex) with a clear dependence on M⋆ ¹⁰. Our two-parameter M⋆ max–M⋆ ¹⁰ description provides a more accurate picture of the galaxy–halo connection at the high-mass end than the simple stellar–halo mass relation (SHMR) and opens a new window to connect the assembly history of haloes with those of central galaxies. The model also predicts that the ex situ component dominates the mass profiles of galaxies at r < 10 kpc for log M⋆ ≥ 11.7. The code used for this paper is available online https://github.com/dr-guangtou/asap
The tidal disruption of the Sagittarius dwarf galaxy has generated a spectacular stream of stars wrapping around the entire Galaxy. We use data from Gaia and the H3 Stellar Spectroscopic Survey to identify 823 high-quality Sagittarius members based on their angular momenta. The H3 Survey is largely unbiased in metallicity, and so our sample of Sagittarius members is similarly unbiased. Stream stars span a wide range in [Fe/H] from -0.2 to ≍-3.0, with a mean overall metallicity of ⟨ [Fe/{H}]⟩ =-0.99 . We identify a strong metallicity dependence to the kinematics of the stream members. At [Fe/H] > -0.8 nearly all members belong to the well-known cold ( {σ }v< 20 km {{s}}⁻¹ ) leading and trailing arms. At intermediate metallicities (-1.9 < [Fe/H] < -0.8) a significant population (24%) emerges of stars that are kinematically offset from the cold arms. These stars also appear to have hotter kinematics. At the lowest metallicities ([Fe/H] ≲ -2), the majority of stars (69%) belong to this kinematically offset diffuse population. Comparison to simulations suggests that the diffuse component was stripped from the Sagittarius progenitor at earlier epochs, and therefore resided at larger radius on average than the colder metal-rich component. We speculate that this kinematically diffuse, low-metallicity population is the stellar halo of the Sagittarius progenitor system.
The North Polar Spur (NPS) is one of the largest structures observed in the Milky Way in both the radio and soft X-rays. While several predictions have been made regarding the origin of the NPS, modelling the structure is difficult without precise distance constraints. In this paper, we determine accurate distances to the southern terminus of the NPS and towards latitudes ranging up to 55°. First, we fit for the distance and extinction to stars towards the NPS using optical and near-infrared photometry and Gaia Data Release 2 astrometry. We model these per-star distance–extinction estimates as being caused by dust screens at unknown distances, which we fit for using a nested sampling algorithm. We then compare the extinction to the Spur derived from our 3D dust modelling with integrated independent measures from XMM–Newton X-ray absorption and H i column density measures. We find that we can account for nearly 100 per cent of the total column density of the NPS as lying within 140 pc for latitudes >26° and within 700 pc for latitudes <11°. Based on the results, we conclude that the NPS is not associated with the Galactic Centre or the Fermi bubbles. Instead, it is likely associated, especially at higher latitudes, with the Scorpius–Centaurus association.
We report the discovery of 15 stars in the H3 survey that lie, in projection, near the tip of the trailing gaseous Magellanic Stream (MS). The stars have Galactocentric velocities <-155 km s-1, Galactocentric distances of ≍40 to 80 kpc (increasing along the MS), and [Fe/H] consistent with that of stars in the Small Magellanic Cloud. These 15 stars comprise 94% (15 of 16) of the H3 observed stars to date that have RGAL > 37.5 kpc, -350 km s-1 < VGSR < -155 km s-1, and are not associated with the Sagittarius Stream. They represent a unique portion of the Milky Way's outer halo phase space distribution function and confirm that unrelaxed structure is detectable even at radii where H3 includes only a few hundred stars. Due to their statistical excess, their close association with the MS and HI compact clouds in the same region, both in position and velocity space, and their plausible correspondence with tidal debris in a published simulation, we identify these stars as debris of past Magellanic Cloud encounters. These stars are evidence for a stellar component of the tidal debris field far from the Clouds themselves and provide unique constraints on the interaction.
Many surveys use maximum-likelihood (ML) methods to fit models when extracting photometry from images. We show that these ML estimators systematically overestimate the flux as a function of the signal-to-noise ratio and the number of model parameters involved in the fit. This bias is substantially worse for resolved sources: while a 1% bias is expected for a 10σ point source, a 10σ resolved galaxy with a simplified Gaussian profile suffers a 2.5% bias. This bias also behaves differently depending how multiple bands are used in the fit: simultaneously fitting all bands leads the flux bias to become roughly evenly distributed between them, while fixing the position in “non-detection” bands (i.e., forced photometry) gives flux estimates in those bands that are biased low, compounding a bias in derived colors. We show that these effects are present in idealized simulations, outputs from the Hyper Suprime-Cam fake-object pipeline (SynPipe), and observations from Sloan Digital Sky Survey Stripe 82. Prescriptions to correct for the ML bias in flux, and its uncertainty, are provided.
The spectroscopic and photometric signals of the star-to-star abundance variations found in globular clusters seem to be correlated with global parameters like the cluster's metallicity, mass, and age. Understanding this behaviour could bring us closer to the origin of these intriguing abundance spreads. In this work we use deep HST photometry to look for evidence of abundance variations in the main sequence of a young massive cluster NGC 419 (∼105 M☉, ∼1.4 Gyr). Unlike previous studies, here we focus on stars in the same mass range found in old globulars (∼0.75-1 M☉), where light elements variations are detected. We find no evidence for N abundance variations among these stars in the Un - B and U - B colour-magnitude diagrams of NGC 419. This is at odds with the N variations found in old globulars like 47 Tuc, NGC 6352, and NGC 6637 with similar metallicity to NGC 419. Although the signature of the abundance variations characteristic of old globulars appears to be significantly smaller or absent in this young cluster, we cannot conclude if this effect is mainly driven by its age or its mass.
We present a new three-dimensional map of dust reddening, based on Gaia parallaxes and stellar photometry from Pan-STARRS 1 and 2MASS. This map covers the sky north of a decl. of -30°, out to a distance of a few kiloparsecs. This new map contains three major improvements over our previous work. First, the inclusion of Gaia parallaxes dramatically improves distance estimates to nearby stars. Second, we incorporate a spatial prior that correlates the dust density across nearby sightlines. This produces a smoother map, with more isotropic clouds and smaller distance uncertainties, particularly to clouds within the nearest kiloparsec. Third, we infer the dust density with a distance resolution that is four times finer than in our previous work, to accommodate the improvements in signal-to-noise enabled by the other improvements. As part of this work, we infer the distances, reddenings, and types of 799 million stars. (Our 3D dust map can be accessed at doi:<A href="https://doi.org/10.7910/DVN/2EJ9TX">10.7910/DVN/2EJ9TX</A> or through the Python package dustmaps, while our catalog of stellar parameters can be accessed at doi:<A href="https://doi.org/10.7910/DVN/AV9GXO">10.7910/DVN/AV9GXO</A>. More information about the map, as well as an interactive viewer, can be found at <A href="http://argonaut.skymaps.info">argonaut.skymaps.info</A>.) We obtain typical reddening uncertainties that are ∼30% smaller than those reported in the Gaia DR2 catalog, reflecting the greater number of photometric passbands that enter into our analysis.
Nonparametric star formation histories (SFHs) have long promised to be the “gold standard” for galaxy spectral energy distribution (SED) modeling as they are flexible enough to describe the full diversity of SFH shapes, whereas parametric models rule out a significant fraction of these shapes a priori. However, this flexibility is not fully constrained even with high-quality observations, making it critical to choose a well-motivated prior. Here, we use the SED-fitting code Prospector to explore the effect of different nonparametric priors by fitting SFHs to mock UV–IR photometry generated from a diverse set of input SFHs. First, we confirm that nonparametric SFHs recover input SFHs with less bias and return more accurate errors than do parametric SFHs. We further find that, while nonparametric SFHs robustly recover the overall shape of the input SFH, the primary determinant of the size and shape of the posterior star formation rate as a function of time (SFR( t )) is the choice of prior, rather than the photometric noise. As a practical demonstration, we fit the UV–IR photometry of ∼6000 galaxies from the Galaxy and Mass Assembly survey and measure scatters between priors to be 0.1 dex in mass, 0.8 dex in SFR 100 Myr , and 0.2 dex in mass-weighted ages, with the bluest star-forming galaxies showing the most sensitivity. An important distinguishing characteristic for nonparametric models is the characteristic timescale for changes in SFR( t ). This difference controls whether galaxies are assembled in bursts or in steady-state star formation, corresponding respectively to (feedback-dominated/accretion-dominated) models of galaxy formation and to (larger/smaller) confidence intervals derived from SED fitting. High-quality spectroscopy has the potential to further distinguish between these proposed models of SFR( t ).
We measure cosmic weak lensing shear power spectra with the Subaru Hyper Suprime-Cam (HSC) survey first-year shear catalog covering 137 deg2 of the sky. Thanks to the high effective galaxy number density of ∼17 arcmin-2, even after conservative cuts such as a magnitude cut of i < 24.5 and photometric redshift cut of 0.3 ≤ z ≤ 1.5, we obtain a high-significance measurement of the cosmic shear power spectra in four tomographic redshift bins, achieving a total signal-to-noise ratio of 16 in the multipole range 300 ≤ ℓ ≤ 1900. We carefully account for various uncertainties in our analysis including the intrinsic alignment of galaxies, scatters and biases in photometric redshifts, residual uncertainties in the shear measurement, and modeling of the matter power spectrum. The accuracy of our power spectrum measurement method as well as our analytic model of the covariance matrix are tested against realistic mock shear catalogs. For a flat Λ cold dark matter model, we find S 8≡ σ _8(Ω _m/0.3)^α =0.800+0.029-0.028 for α = 0.45 (S _8=0.780+0.030-0.033 for α = 0.5) from our HSC tomographic cosmic shear analysis alone. In comparison with Planck cosmic microwave background constraints, our results prefer slightly lower values of S8, although metrics such as the Bayesian evidence ratio test do not show significant evidence for discordance between these results. We study the effect of possible additional systematic errors that are unaccounted for in our fiducial cosmic shear analysis, and find that they can shift the best-fit values of S8 by up to ∼0.6 σ in both directions. The full HSC survey data will contain several times more area, and will lead to significantly improved cosmological constraints.
We present a uniform catalog of accurate distances to local molecular clouds informed by the Gaia DR2 data release. Our methodology builds on that of Schlafly et al. First, we infer the distance and extinction to stars along sightlines toward the clouds using optical and near-infrared photometry. When available, we incorporate knowledge of the stellar distances obtained from Gaia DR2 parallax measurements. We model these per-star distance–extinction estimates as being caused by a dust screen with a 2D morphology derived from Planck at an unknown distance, which we then fit for using a nested sampling algorithm. We provide updated distances to the Schlafly et al. sightlines toward the Dame et al. and Magnani et al. clouds, finding good agreement with the earlier work. For a subset of 27 clouds, we construct interactive pixelated distance maps to further study detailed cloud structure, and find several clouds which display clear distance gradients and/or are comprised of multiple components. We use these maps to determine robust average distances to these clouds. The characteristic combined uncertainty on our distances is ≈5%–6%, though this can be higher for clouds at greater distances, due to the limitations of our single-cloud model.
Galaxy observations are influenced by many physical parameters: stellar masses, star formation rates (SFRs), star formation histories (SFHs), metallicities, dust, black hole activity, and more. As a result, inferring accurate physical parameters requires high-dimensional models that capture or marginalize over this complexity. Here we reassess inferences of galaxy stellar masses and SFRs using the 14-parameter physical model Prospector- α built in the Prospector Bayesian inference framework. We fit the photometry of 58,461 galaxies from the 3D-HST catalogs at 0.5 < z < 2.5. The resulting stellar masses are ∼0.1-0.3 dex larger than the fiducial masses while remaining consistent with dynamical constraints. This change is primarily due to the systematically older SFHs inferred with Prospector. The SFRs are ∼0.1-1+ dex lower than UV+IR SFRs, with the largest offsets caused by emission from “old” (t > 100 Myr) stars. These new inferences lower the observed cosmic SFR density by ∼0.2 dex and increase the observed stellar mass growth by ∼0.1 dex, finally bringing these two quantities into agreement and implying an older, more quiescent universe than found by previous studies at these redshifts. We corroborate these results by showing that the Prospector- α SFHs are both more physically realistic and much better predictors of the evolution of the stellar mass function. Finally, we highlight examples of observational data that can break degeneracies in the current model; these observations can be incorporated into priors in future models to produce new and more accurate physical parameters.
Markov Chain Monte Carlo (MCMC) methods have become a cornerstone of many modern scientific analyses by providing a straightforward approach to numerically estimate uncertainties in the parameters of a model using a sequence of random samples. This article provides a basic introduction to MCMC methods by establishing a strong conceptual understanding of what problems MCMC methods are trying to solve, why we want to use them, and how they work in theory and in practice. To develop these concepts, I outline the foundations of Bayesian inference, discuss how posterior distributions are used in practice, explore basic approaches to estimate posterior-based quantities, and derive their link to Monte Carlo sampling and MCMC. Using a simple toy problem, I then demonstrate how these concepts can be used to understand the benefits and drawbacks of various MCMC approaches. Exercises designed to highlight various concepts are also included throughout the article.
We present a flexible, detailed model for the evolution of galactic discs in a cosmological context since z ≈ 4, including a physically motivated model for radial transport of gas and stars within galactic discs. This expansion beyond traditional semi-analytic models that do not include radial structure, or include only a prescribed radial structure, enables us to study the internal structure of disc galaxies and the processes that drive it. In order to efficiently explore the large parameter space allowed by this model, we construct a neural-network-based emulator that can quickly return a reasonable approximation for many observables we can extract from the model, e.g. the star formation rate or the half-mass stellar radius, at different redshifts. We employ the emulator to constrain the model parameters with Bayesian inference by comparing its predictions to 11 observed galaxy scaling relations at a variety of redshifts. The constrained models agree well with observations, both those used to fit the data and those not included in the fitting procedure. These models will be useful theoretical tools for understanding the increasingly detailed observational data sets from Integral Field Units (IFUs).
Although photometric redshifts (photo-z's) are crucial ingredients for current and upcoming large-scale surveys, the high-quality spectroscopic redshifts currently available to train, validate, and test them are substantially non-representative in both magnitude and colour. We investigate the nature and structure of this bias by tracking how objects from a heterogeneous training sample contribute to photo-z predictions as a function of magnitude and colour, and illustrate that the underlying redshift distribution at fixed colour can evolve strongly as a function of magnitude. We then test the robustness of the galaxy-galaxy lensing signal in 120 deg2 of HSC-SSP DR1 data to spectroscopic completeness and photo-z biases, and find that their impacts are sub-dominant to current statistical uncertainties. Our methodology provides a framework to investigate how spectroscopic incompleteness can impact photo-z-based weak lensing predictions in future surveys such as LSST and WFIRST.
We present the first measurement of cross-correlation between the lensing potential, reconstructed from cosmic microwave background (CMB) polarization data, and the cosmic shear field from galaxy shapes. This measurement is made using data from the POLARBEAR CMB experiment and the Subaru Hyper Suprime-Cam (HSC) survey. By analyzing an 11 deg2 overlapping region, we reject the null hypothesis at 3.5σ and constrain the amplitude of the cross-spectrum to {A}lens}=1.70+/- 0.48, where {A}lens} is the amplitude normalized with respect to the Planck 2018 prediction, based on the flat Λ cold dark matter cosmology. The first measurement of this cross-spectrum without relying on CMB temperature measurements is possible owing to the deep POLARBEAR map with a noise level of ∼6 μK arcmin, as well as the deep HSC data with a high galaxy number density of {n}g=23 {arcmin}}-2. We present a detailed study of the systematics budget to show that residual systematics in our results are negligibly small, which demonstrates the future potential of this cross-correlation technique.
In Paper I we studied the determination of the delay time distribution (DTD) of binary neutron stars (BNSs) through scaling relations between halo/stellar mass and the star formation history (SFH) of galaxies hosting gravitational-wave (GW) events in the local universe. Here we explore how a detailed reconstruction of the individual SFHs of BNS merger host galaxies can improve on the use of the scaling relations. We use galaxies from the Galaxy and Mass Assembly survey, which is mass complete at M * > 109 M ☉ in the redshift range 0.05 < z < 0.08. We use the reconstructed SFHs derived from the Prospector code for two distinct sets of priors (favoring continuous and bursty SFHs), and convolve those with power-law DTDs characterized by an index Γ and a minimum delay time t min. We find that with this approach O (100)-O (300) host galaxies are required to constrain the DTD parameters, with the number depending on the choice of SFH prior and on the parameters of the true DTD. We further show that using only the host galaxies of BNS mergers, as opposed to the full population of potential host galaxies in the relevant cosmic volume, leads to a minor bias in the recovered DTD parameters. The required host galaxy sample size is nearly an order of magnitude smaller relative to the approach of using scaling relations, and we expect such a host galaxy sample to be collected within a decade or two, prior to the advent of third-generation GW detectors.
We present a comprehensive study of the applications of the pixel color–magnitude diagram (pCMD) technique for measuring star formation histories (SFHs) and other stellar population parameters of galaxies, and we demonstrate that the technique can also constrain distances. SFHs have previously been measured through either the modeling of resolved-star CMDs or of integrated-light spectral energy distributions, yet neither approach can easily be applied to galaxies in the “semi-resolved regime.” The pCMD technique has previously been shown to have the potential to measure stellar populations and SFHs in semi-resolved galaxies. Here we present Pixel Color–Magnitude Diagrams with Python (PCMDPy), a graphics processing unit (GPU)-accelerated package that makes significant computational improvements to the original code and includes more realistic physical models. These advances include the simultaneous fitting of distance, modeling a Gaussian metallicity distribution function, and an observationally motivated dust model. GPU acceleration allows these more realistic models to be fit roughly 7× faster than the simpler models in the original code. We present results from a suite of mock tests, showing that with proper model assumptions, the code can simultaneously recover SFH, [ Fe / H ] , distance, and dust extinction. Our results suggest the code, applied to observations with Hubble Space Telescope-like resolution, should constrain these properties with high precision within 10 Mpc and can be applied to systems out to as far as 100 Mpc. pCMDs open a new window to studying the stellar populations of many galaxies that cannot be readily studied through other means.
Over the past century, major advances in astronomy and astrophysics have been largely driven by improvements in instrumentation and data collection. With the amassing of high quality data from new telescopes, and especially with the advent of deep and large astronomical surveys, it is becoming clear that future advances will also rely heavily on how those data are analyzed and interpreted. New methodologies derived from advances in statistics, computer science, and machine learning are beginning to be employed in sophisticated investigations that are not only bringing forth new discoveries, but are placing them on a solid footing. Progress in wide-field sky surveys, interferometric imaging, precision cosmology, exoplanet detection and characterization, and many subfields of stellar, Galactic and extragalactic astronomy, has resulted in complex data analysis challenges that must be solved to perform scientific inference. Research in astrostatistics and astroinformatics will be necessary to develop the state-of-the-art methodology needed in astronomy. Overcoming these challenges requires dedicated, interdisciplinary research. We recommend: (1) increasing funding for interdisciplinary projects in astrostatistics and astroinformatics; (2) dedicating space and time at conferences for interdisciplinary research and promotion; (3) developing sustainable funding for long-term astrostatisics appointments; and (4) funding infrastructure development for data archives and archive support, state-of-the-art algorithms, and efficient computing.
Software is critical to astronomical research. Sharing and sustaining astronomical software has long-term impacts on scientific outcomes. However, support for this has been uneven, creating significant risks. Thus, we highlight changes that will enable a sustainable software sharing system for astronomy and astrophysics in the next decade.
This white paper describes the impact of mapping 3D dust properties in the Milky Way, including extinction curve behavior and dynamics, and the observations needed to achieve these goals.
Hyper Suprime-Cam (HSC) is a wide-field imaging camera on the prime focus of the 8.2-m Subaru telescope on the summit of Mauna Kea in Hawaii. A team of scientists from Japan, Taiwan, and Princeton University is using HSC to carry out a 300-night multi-band imaging survey of the high-latitude sky. The survey includes three layers: the Wide layer will cover 1400 deg2 in five broad bands (grizy), with a 5 σ point-source depth of r ≈ 26. The Deep layer covers a total of 26 deg2 in four fields, going roughly a magnitude fainter, while the UltraDeep layer goes almost a magnitude fainter still in two pointings of HSC (a total of 3.5 deg2). Here we describe the instrument, the science goals of the survey, and the survey strategy and data processing. This paper serves as an introduction to a special issue of the Publications of the Astronomical Society of Japan, which includes a large number of technical and scientific papers describing results from the early phases of this survey.
The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) is a three-layered imaging survey aimed at addressing some of the most important outstanding questions in astronomy today, including the nature of dark matter and dark energy. The survey has been awarded 300 nights of observing time at the Subaru Telescope, and it started in 2014 March. This paper presents the first public data release of HSC-SSP. This release includes data taken in the first 1.7 yr of observations (61.5 nights), and each of the Wide, Deep, and UltraDeep layers covers about 108, 26, and 4 square degrees down to depths of i ∼ 26.4, ∼26.5, and ∼27.0 mag, respectively (5 σ for point sources). All the layers are observed in five broad bands (grizy), and the Deep and UltraDeep layers are observed in narrow bands as well. We achieve an impressive image quality of 0{′′.}6 in the i band in the Wide layer. We show that we achieve 1%–2% point spread function (PSF) photometry (root mean square) both internally and externally (against Pan-STARRS1), and ∼10 mas and 40 mas internal and external astrometric accuracy, respectively. Both the calibrated images and catalogs are made available to the community through dedicated user interfaces and database servers. In addition to the pipeline products, we also provide value-added products such as photometric redshifts and a collection of public spectroscopic redshifts. Detailed descriptions of all the data can be found online. The data release website is https://hsc-release.mtk.nao.ac.jp.
Photometric redshifts are a key component of many science objectives in the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). In this paper, we describe and compare the codes used to compute photometric redshifts for HSC-SSP, how we calibrate them, and the typical accuracy we achieve with the HSC five-band photometry (grizy). We introduce a new point estimator based on an improved loss function and demonstrate that it works better than other commonly used estimators. We find that our photo-z's are most accurate at 0.2 ≲ zphot ≲ 1.5, where we can straddle the 4000 Å break. We achieve σ[∆zphot/(1 + zphot)] ∼ 0.05 and an outlier rate of about 15% for galaxies down to i = 25 within this redshift range. If we limit ourselves to a brighter sample of i < 24, we achieve σ ∼ 0.04 and ∼8% outliers. Our photo-z's should thus enable many science cases for HSC-SSP. We also characterize the accuracy of our redshift probability distribution function (PDF) and discover that some codes over-/underestimate the redshift uncertainties, which has implications for N(z) reconstruction. Our photo-z products for the entire area in Public Data Release 1 are publicly available, and both our catalog products (such as point estimates) and full PDFs can be retrieved from the data release site, "https://hsc-release.mtk.nao.ac.jp/".
We present and characterize the catalog of galaxy shape measurements that will be used for cosmological weak lensing measurements in the Wide layer of the first year of the Hyper Suprime-Cam (HSC) survey. The catalog covers an area of 136.9 deg2 split into six fields, with a mean i-band seeing of 0{″.}58 and 5σ point-source depth of i ∼ 26. Given conservative galaxy selection criteria for first-year science, the depth and excellent image quality results in unweighted and weighted source number densities of 24.6 and 21.8 arcmin-2, respectively. We define the requirements for cosmological weak lensing science with this catalog, then focus on characterizing potential systematics in the catalog using a series of internal null tests for problems with point-spread function (PSF) modeling, shear estimation, and other aspects of the image processing. We find that the PSF models narrowly meet requirements for weak lensing science with this catalog, with fractional PSF model size residuals of approximately 0.003 (requirement: 0.004) and the PSF model shape correlation function ρ1 < 3 × 10-7 (requirement: 4 × 10-7) at 0.5° scales. A variety of galaxy shape-related null tests are statistically consistent with zero, but star-galaxy shape correlations reveal additive systematics on >1° scales that are sufficiently large as to require mitigation in cosmic shear measurements. Finally, we discuss the dominant systematics and the planned algorithmic changes to reduce them in future data reductions.
We present an optically-selected cluster catalog from the Hyper Suprime-Cam (HSC) Subaru Strategic Program. The HSC images are sufficiently deep to detect cluster member galaxies down to M* ∼ 1010.2 M☉ even at z ∼ 1, allowing a reliable cluster detection at such high redshifts. We apply the CAMIRA algorithm to the HSC Wide S16A dataset covering ∼232 deg2 to construct a catalog of 1921 clusters at redshift 0.1 < z < 1.1 and richness N_mem>15 that roughly corresponds to M200m ≳ 1014 h-1 M☉. We confirm good cluster photometric redshift performance, with the bias and the scatter in ∆z/(1 + z) being better than 0.005 and 0.01, respectively, over most of the redshift range. We compare our cluster catalog with large X-ray cluster catalogs from the XXL and XMM-LSS (the XMM Large Scale Structure) surveys and find good correlation between richness and X-ray properties.We also study the mis-centering effect from the distribution of offsets between optical and X-ray cluster centers. We confirm the high (>0.9) completeness and purity for high-mass clusters by analyzing mock galaxy catalogs.
We present a new technique to determine distances to major star-forming regions across the Perseus Molecular Cloud, using a combination of stellar photometry, astrometric data, and 12CO spectral-line maps. Incorporating the Gaia DR2 parallax measurements when available, we start by inferring the distance and reddening to stars from their Pan-STARRS1 and Two Micron All Sky Survey photometry, based on a technique presented by Green et al. and implemented in their 3D “Bayestar” dust map of three-quarters of the sky. We then refine their technique by using the velocity slices of a CO spectral cube as dust templates and modeling the cumulative distribution of dust along the line of sight toward these stars as a linear combination of the emission in the slices. Using a nested sampling algorithm, we fit these per-star distance-reddening measurements to find the distances to the CO velocity slices toward each star-forming region. This results in distance estimates explicitly tied to the velocity structure of the molecular gas. We determine distances to the B5, IC 348, B1, NGC 1333, L1448, and L1451 star-forming regions and find that individual clouds are located between ≈275 and 300 pc, with typical combined uncertainties of ≈5%. We find that the velocity gradient across Perseus corresponds to a distance gradient of about 25 pc, with the eastern portion of the cloud farther away than the western portion. We determine an average distance to the complex of 294 ± 17 pc, about 60 pc further than the distance derived to the western portion of the cloud using parallax measurements of water masers associated with young stellar objects. The method we present is not limited to the Perseus Complex, but may be applied anywhere on the sky with adequate CO data in the pursuit of more accurate 3D maps of molecular clouds in the solar neighborhood and beyond.
We present optimized source galaxy selection schemes for measuring cluster weak lensing (WL) mass profiles unaffected by cluster member dilution from the Subaru Hyper Suprime-Cam Strategic Survey Program (HSC-SSP). The ongoing HSC-SSP survey will uncover thousands of galaxy clusters to z ≲ 1.5. In deriving cluster masses via WL, a critical source of systematics is contamination and dilution of the lensing signal by cluster members, and by foreground galaxies whose photometric redshifts are biased. Using the first-year CAMIRA catalog of ∼900 clusters with richness larger than 20 found in ∼140 deg2 of HSC-SSP data, we devise and compare several source selection methods, including selection in color-color space (CC-cut), and selection of robust photometric redshifts by applying constraints on their cumulative probability distribution function (P-cut). We examine the dependence of the contamination on the chosen limits adopted for each method. Using the proper limits, these methods give mass profiles with minimal dilution in agreement with one another. We find that not adopting either the CC-cut or P-cut methods results in an underestimation of the total cluster mass (13% ± 4%) and the concentration of the profile (24% ± 11%). The level of cluster contamination can reach as high as ∼10% at R ≈ 0.24 Mpc/h for low-z clusters without cuts, while employing either the P-cut or CC-cut results in cluster contamination consistent with zero to within the 0.5% uncertainties. Our robust methods yield a ∼60 σ detection of the stacked CAMIRA surface mass density profile, with a mean mass of M200c = [1.67 ± 0.05(stat)] × 1014 M☉/h.
We propose a method to substantially increase the flexibility and power of template fitting-based photometric redshifts by transforming a large number of galaxy spectral templates into a corresponding collection of 'fuzzy archetypes' using a suitable set of perturbative priors designed to account for empirical variation in dust attenuation and emission-line strengths. To bypass widely separated degeneracies in parameter space (e.g. the redshift-reddening degeneracy), we train self-organizing maps (SOMs) on large 'model catalogues' generated from Monte Carlo sampling of our fuzzy archetypes to cluster the predicted observables in a topologically smooth fashion. Subsequent sampling over the SOM then allows full reconstruction of the relevant probability distribution functions (PDFs). This combined approach enables the multimodal exploration of known variation among galaxy spectral energy distributions with minimal modelling assumptions. We demonstrate the power of this approach to recover full redshift PDFs using discrete Markov chain Monte Carlo sampling methods combined with SOMs constructed from Large Synoptic Survey Telescope ugrizY and Euclid YJH mock photometry.
We propose a method to substantially increase the flexibility and power of template fitting-based photometric redshifts by transforming a large number of galaxy spectral templates into a corresponding collection of ‘fuzzy archetypes’ using a suitable set of perturbative priors designed to account for empirical variation in dust attenuation and emission-line strengths. To bypass widely separated degeneracies in parameter space (e.g. the redshift-reddening degeneracy), we train self-organizing maps (SOMs) on large ‘model catalogues’ generated from Monte Carlo sampling of our fuzzy archetypes to cluster the predicted observables in a topologically smooth fashion. Subsequent sampling over the SOM then allows full reconstruction of the relevant probability distribution functions (PDFs). This combined approach enables the multimodal exploration of known variation among galaxy spectral energy distributions with minimal modelling assumptions. We demonstrate the power of this approach to recover full redshift PDFs using discrete Markov chain Monte Carlo sampling methods combined with SOMs constructed from Large Synoptic Survey Telescope ugrizY and Euclid YJH mock photometry.
The current hierarchical merging paradigm and ΛCDM predict that the z∼ 4-8 universe should be a time in which the most massive galaxies are transitioning from their initial halo assembly to the later baryonic evolution seen in star-forming galaxies and quasars. However, no evidence of this transition has been found in many high-redshift galaxy surveys including CFHTLS, Cosmic Assembly Near-infrared Deep Extragalactic Survey (CANDELS), and Spitzer Large Area Survey with Hyper-Suprime-Cam (SPLASH), which were the first studies to probe the high-mass end at these redshifts. Indeed, if halo mass to stellar mass ratios estimated at lower-redshift continue to z∼ 6-8, CANDELS and SPLASH report several orders of magnitude more M∼ {10}12-13{M}☉ halos than is possible to have been formed by those redshifts, implying that these massive galaxies formed impossibly early. We consider various systematics in the stellar synthesis models used to estimate physical parameters and possible galaxy formation scenarios in an effort to reconcile observation with theory. Although known uncertainties can greatly reduce the disparity between recent observations and cold dark matter merger simulations, there remains considerable tension with current theory even if taking the most conservative view of the observations.
Using a 4D grid of ∼2 million model parameters (∆z = 0.005) adapted from Cosmological Origins Survey photometric redshift (photo-z) searches, we investigate the general properties of template-based photo-z likelihood surfaces. We find these surfaces are filled with numerous local minima and large degeneracies that generally confound simplistic gradient-descent optimization schemes. We combine ensemble Markov Chain Monte Carlo sampling with simulated annealing to robustly and efficiently explore these surfaces in approximately constant time. Using a mock catalogue of 384 662 objects, we show our approach samples ∼40 times more efficiently compared to a `brute-force' counterpart while maintaining similar levels of accuracy. Our results represent first steps towards designing template-fitting photo-z approaches limited mainly by memory constraints rather than computation time.
Calibrating the photometric redshifts of ≳109 galaxies for upcoming weak lensing cosmology experiments is a major challenge for the astrophysics community. The path to obtaining the required spectroscopic redshifts for training and calibration is daunting, given the anticipated depths of the surveys and the difficulty in obtaining secure redshifts for some faint galaxy populations. Here we present an analysis of the problem based on the self-organizing map, a method of mapping the distribution of data in a high-dimensional space and projecting it onto a lower-dimensional representation. We apply this method to existing photometric data from the COSMOS survey selected to approximate the anticipated Euclid weak lensing sample, enabling us to robustly map the empirical distribution of galaxies in the multidimensional color space defined by the expected Euclid filters. Mapping this multicolor distribution lets us determine where—in galaxy color space—redshifts from current spectroscopic surveys exist and where they are systematically missing. Crucially, the method lets us determine whether a spectroscopic training sample is representative of the full photometric space occupied by the galaxies in a survey. We explore optimal sampling techniques and estimate the additional spectroscopy needed to map out the color-redshift relation, finding that sampling the galaxy distribution in color space in a systematic way can efficiently meet the calibration requirements. While the analysis presented here focuses on the Euclid survey, similar analysis can be applied to other surveys facing the same calibration challenge, such as DES, LSST, and WFIRST.
Using a compilation of 25 studies from the literature, we investigate the evolution of the star-forming galaxy (SFG) Main Sequence (MS) in stellar mass and star formation rate (SFR) out to z ∼ 6. After converting all observations to a common set of calibrations, we find a remarkable consensus among MS observations (∼ 0.1 dex 1σ interpublication scatter). By fitting for time evolution of the MS in bins of constant mass, we deconvolve the observed scatter about the MS within each observed redshift bins. After accounting for observed scatter between different SFR indicators, we find the width of the MS distribution is ∼ 0.2 dex and remains constant over cosmic time. Our best fits indicate the slope of the MS is likely time-dependent, with our best fit \logSFR(M_*,t) = (0.84 ± 0.02 - 0.026 ± 0.003 × t) log M_* - (6.51 ± 0.24 - 0.11 ± 0.03 × t), with t the age of the Universe in Gyr. We use our fits to create empirical evolutionary tracks in order to constrain MS galaxy star formation histories (SFHs), finding that (1) the most accurate representations of MS SFHs are given by delayed-τ models, (2) the decline in fractional stellar mass growth for a "typical" MS galaxy today is approximately linear for most of its lifetime, and (3) scatter about the MS can be generated by galaxies evolving along identical evolutionary tracks assuming an initial 1σ spread in formation times of ∼ 1.4 Gyr.
Using the first 50% of data collected for the Spitzer Large Area Survey with Hyper-Suprime-Cam observations on the 1.8 deg2 Cosmological Evolution Survey we estimate the masses and star formation rates of 3398 M * > 1010 M ☉ star-forming galaxies at 4 < z < 6 with a substantial population up to M * >~ 1011.5 M ☉. We find that the strong correlation between stellar mass and star formation rate seen at lower redshift (the "main sequence" of star-forming galaxies) extends to z ~ 6. The observed relation and scatter is consistent with a continued increase in star formation rate at fixed mass in line with extrapolations from lower-redshift observations. It is difficult to explain this continued correlation, especially for the most massive systems, unless the most massive galaxies are forming stars near their Eddington-limited rate from their first collapse. Furthermore, we find no evidence for moderate quenching at higher masses, indicating quenching either has not occurred prior to z ~ 6 or else occurs rapidly, so that few galaxies are visible in transition between star-forming and quenched.
Recent observations indicate a remarkable similarity in the properties of evolving galaxies at fixed mass and redshift, prompting us to consider the possibility that most galaxies may evolve with a common history encompassing star formation, quasar accretion, and eventual quiescence. We quantify this by defining a synchronization timescale for galaxies as a function of mass and redshift that characterizes the extent to which different galaxies of a common mass are evolving in the same manner at various cosmic epochs. We measure this synchronization timescale using nine different star-forming galaxy observations from the literature and Sloan Digital Sky Survey quasar observations spanning 0 < z <~ 6. Surprisingly, this synchronization timescale is a constant, approximately 1.5 Gyr for all combinations of mass and time. We also find that the ratio between the stellar mass of galaxies turning off star formation and black hole mass of turnoff quasars is approximately 30:1, much lower than the 500:1 for quiescent galaxies at low redshift. As a result, we propose a model in which the star-forming "main sequence," analogous quasar behavior, and other observations form a galactic evolution "main sequence," in which star formation occurs earliest, followed by supermassive black hole accretion, and feedback between the two are dominated by deterministic rather than stochastic processes.
Middle-aged, cooling neutron stars are observed both as relatively rapidly spinning radio pulsars and as more slowly spinning, strongly magnetized isolated neutron stars (INSs), which stand out by their thermal X-ray spectra. The difference between the two classes may be that the INSs initially had much stronger magnetic fields, which decayed. To test this, we used the Chandra X-ray Observatory to observe 1RXS J072559.8-261229, a possible X-ray counterpart to PSR J0726-2612, which, with its 3.44 s period and 3 × 1013 G inferred magnetic field strength, is the nearest and least extincted among the possible slowly spinning, strong-field INS progenitors (it likely is in the Gould Belt, at ~1 kpc). We confirm the identification and find that the pulsar has a spectrum consistent with being purely thermal, with blackbody temperature kT = 87 ± 5 eV and radius R = 5.7+2.6 - 1.3 km at a distance of 1 kpc. We detect sinusoidal pulsations at twice the radio period with a semi-amplitude of 27% ± 5%. The properties of PSR J0726-2612 strongly resemble those of the INSs, except for its much shorter characteristic age of 200 kyr (instead of several Myr). We conclude that PSR J0726-2612 is indeed an example of a young INS, one that started with a magnetic field strength on the low end of those inferred for the INSs, and that, therefore, decayed by a relatively small amount. Our results suggest that the long-period, strong-field pulsars and the INSs are members of the same class, and open up new opportunities to understand the puzzling X-ray and optical emission of the INSs through radio observations of PSR J0726-2612.
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