arXiv subjects

Gregory M. Green

Publications and source records attributed to Gregory M. Green.

At least 19 recordsLinked to original sources

arXiv2026

Variations in the Milky Way's Stellar Mass Function at [Fe/H] < -1

We present the first determination of the Galactic stellar mass function (MF) for low-mass stars (0.2-0.5 M_sun) at metallicities [Fe/H] < -1. A sample of ~53,000 stars was selected as metal-poor on the basis of both their halo-like orbits and their spectroscopic [Fe/H] from Gaia DR3 BP/RP (XP) spectra. These metallicity estimates for low-mass stars were enabled by calibrating Gaia XP spectra with stellar parameters from SDSS-V. For -1.5 < [Fe/H] < -1, we find that the MF below 0.5 M_sun exhibits a "bottom-heavy" power-law slope of alpha ~ -1.6. We tentatively find that at even lower metallicities, the MF becomes very bottom-light, with a near-flat power-law slope of alpha ~ 0 that implies a severe deficit of low-mass stars. This metallicity-dependent variation is insensitive to the adopted stellar evolution model. These results show that the Galactic low-mass MF is not universal, with variations in the metal-poor regime. A further calibration of XP metallicities in the regime of M < 0.5 M_sun and [Fe/H] < -1.5 will be essential to verify these tentative low-metallicity trends.

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arXiv2025

Quadrupole signature as a kinematic diagnostic to constrain bar properties : implications for the Milky Way

The presence of a 'butterfly' or a quadrupole structure in the stellar mean radial velocity ($ $) field of the Milky Way is well known from the Gaia and the APOGEE surveys. Past studies indicated that a stellar bar can excite such a quadrupole feature in the $< V_R >$ distribution. However, a systematic study investigating the co-evolution of bar and quadrupole structure is largely missing. Furthermore, whether this quadrupole structure in $ $ can be used as a robust kinematic diagnostic to constrain bar properties, particularly for the Milky Way, is still beyond our grasp. Here, we investigate the bar-induced quadrupole feature using a suite of isolated $N$-body models forming prominent bars and a sample of Milky Way-like barred galaxies from the TNG50 cosmological simulation. We demonstrate that the properties of the quadrupole (strength, length, and orientation) are strongly correlated with the bar properties, regardless of the choice of the thin/thick disc stars; thereby making the quadrupole feature an excellent kinematic diagnostic for constraining the bar properties. In presence of spirals, the estimator which takes into account the phase-angle of $m = 4$ Fourier moment, serves as a more appropriate estimator for measuring the length of the quadrupole. Further, we constructed a novel Gaia-like mock dataset from a simulated bar model while incorporating the dust extinction and the broad trends of observational errors of the Gaia survey. The quadrupole properties (strength and length) estimated from those Gaia-like mock data are larger ($\sim 35-45$ percent) when compared with their true values. We showed that the majority of this effect is due to the uncertainty in parallax measurement. This demonstrates that the quadrupole structure in Gaia data is likely a result of dominant Gaia parallax errors/biases, almost masking the true inherent signature of the MW bar.

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arXiv2025

A Deep, High-Angular Resolution 3D Dust Map of the Southern Galactic Plane

We present a deep, high-angular resolution 3D dust map of the southern Galactic plane over $239^\circ < \ell < 6^\circ$ and $|b| < 10^\circ$ built on photometry from the DECaPS2 survey, in combination with photometry from VVV, 2MASS, and unWISE and parallaxes from Gaia DR3 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 MIST stellar models. Our resultant 3D dust map has an angular resolution of $1'$, 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 $d \approx$ 10 kpc from the Sun. The map is sensitive up to a maximum extinction of roughly $A_V \approx 12$ 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 $|b| < 10^\circ$. 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.

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arXiv2025

Deep Potential: Recovering the gravitational potential and local pattern speed in the solar neighborhood with GDR3 using normalizing flows

The gravitational potential of the Milky Way encodes information about the distribution of all matter -- including dark matter -- throughout the Galaxy. Gaia data release 3 has revealed a complex structure that necessitates flexible models of the Galactic gravitational potential. We make use of a sample of 5.6 million upper-main-sequence stars to map the full 3D gravitational potential in a one-kiloparsec radius from the Sun using a data-driven approach called ``Deep Potential''. This method makes minimal assumptions about the dynamics of the Galaxy -- that the stars are a collisionless system that is statistically stationary in a rotating frame (with pattern speed to be determined). We model the distribution of stars in 6D phase space using a normalizing flow and the gravitational network using a neural network. We recover a local pattern speed of $Ω_p = 28.2\pm0.1\mathrm{\,km/s/kpc}$, a local total matter density of $ρ=0.086\pm0.010\mathrm{\,M_\odot/pc^3}$ and local dark matter density of $ρ_\mathrm{DM}=0.007\pm0.011\mathrm{\,M_\odot/pc^3}$. The full 3D model exhibits spatial fluctuations, which may stem from the model architecture and non-stationarity in the Milky Way.

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arXiv2025

Millions of Main-Sequence Binary Stars from Gaia BP/RP Spectra

We present the main-sequence binary (MSMS) Catalog derived from Gaia Data Release 3 BP/RP (XP) spectra. Leveraging the vast sample of low-resolution Gaia XP spectra, we develop a forward modeling approach that maps stellar mass and photometric metallicity to XP spectra using a neural network. Our methodology identifies binary systems through statistical comparison of single- and binary-star model fits, enabling detection of binaries with mass ratios between 0.4 and 1.0 and flux ratios larger than 0.1. From an initial sample of 35 million stars within 1 kpc, we identify 14 million binary candidates and define a high-confidence "golden sample" of 1 million binary systems. This large, homogeneous sample enables detailed statistical analysis of binary properties across diverse Galactic environments, providing new insights into binary star formation and evolution. In addition, the $χ^2$ comparison allows us to distinguish stars with luminous companions from single stars or binaries with dark companions, such as white dwarfs, neutron stars and black hole candidates, improving our understanding of compact object populations.

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arXiv2025

Correlations between Dust Extinction Features across All Wavelength Scales: From Diffuse Interstellar Bands to R(V)

Understanding variations in the dust extinction curve is imperative for using dust as a tracer of local structure in the interstellar medium, understanding dust chemistry, and observational color corrections where dust is a nuisance parameter. However, the extinction curve is complicated and exhibits features across a wide range of wavelength scales, from narrow atomic lines and diffuse interstellar bands ("DIBs"), to intermediate-scale and very broad structures ("ISS" and "VBS"), and the overall slope of the optical extinction curve, parameterized by R(V). Robust, population-level studies of variations in these features are only now possible with large, all-sky, spectroscopic surveys. However, these features are often studied independently because they require drastically different spectral resolution. In this work, we couple features with disparate wavelength scales by cross-matching precision catalogs of DIB measurements from APOGEE and Gaia RVS with low-resolution extinction-curve measurements from Gaia XP. Using this combination, we show that there are meaningful correlations between the strengths of extinction-curve features across all wavelength scales. We present a model that statistically explains part of the excess scatter in DIB strength versus extinction, and we show variation in line shapes of two DIBs as a function of R(V). We find that most DIBs increase in strength with increasing R(V) and/or increasing strength of the ISS, though we found one DIB that anomalously decreases in strength with increasing R(V). Using the behavior of the ensemble of DIBs in APOGEE, we present this as the first evidence of systematic chemical variation accompanying R(V) variation.

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arXiv2025

Differentiable Stellar Atmospheres with Physics-Informed Neural Networks

We present Kurucz-a1, a physics-informed neural network (PINN) that emulates 1D stellar atmosphere models under Local Thermodynamic Equilibrium (LTE), addressing a critical bottleneck in differentiable stellar spectroscopy. By incorporating hydrostatic equilibrium as a physical constraint during training, Kurucz-a1 creates a differentiable atmospheric structure solver that maintains physical consistency while achieving computational efficiency. Kurucz-a1 can achieve superior hydrostatic equilibrium and more consistent with the solar observed spectra compared to ATLAS-12 itself, demonstrating the advantages of modern optimization techniques. Combined with modern differentiable radiative transfer codes, this approach enables data-driven optimization of universal physical parameters across diverse stellar populations-a capability essential for next-generation stellar astrophysics.

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arXiv2025

The Dust Extinction Curve: Beyond R(V)

The dust extinction curve is typically parameterized by a single variable, R(V), in optical and near-infrared wavelengths. R(V) controls the slope of the extinction-vs.-wavelength curve, and is thought to reflect the grain-size distribution and composition of dust. Low-resolution, flux-calibrated BP/RP spectra from Gaia have allowed the determination of the extinction curve along sightlines to 130 million stars in the Milky Way and Magellanic Clouds. We show that these extinction curves contain more than a single degree of freedom - that is, that they are not simply described by R(V). We identify a number of components that are orthogonal to R(V) variation, and show that these components vary across the sky in coherent patterns that resemble interstellar medium structure. These components encode variation in the 770 nm extinction feature, intermediate-scale and very broad structure, and a newly identified feature at 850 nm, and likely trace both dust composition and local conditions in the interstellar medium. Correlations of the 770 nm and 850 nm features with R(V) suggest that their carriers become more abundant as the carrier of the 2175 Angstrom feature is destroyed. Our 24 million extinction-curve decompositions and feature equivalent-width measurements are publicly available at https://dx.doi.org/10.5281/zenodo.14005028.

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arXiv2025

Deriving Stellar Properties, Distances, and Reddenings using Photometry and Astrometry with BRUTUS

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.

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arXiv2025

Mapping the Milky Way in 5-D with 170 Million Stars

We present "augustus", a catalog of distance, extinction, and stellar parameter estimates to 170 million stars from $14\,{\rm mag} < r < 20\,{\rm mag}$ and with $|b| > 10^\circ$ drawing on a combination of optical to near-IR photometry from Pan-STARRS, 2MASS, UKIDSS, and unWISE along with parallax measurements from \textit{Gaia} DR2 and 3-D dust extinction maps. After applying quality cuts, we find 125 million objects have "high-quality" posteriors with statistical distance uncertainties of $\lesssim 10\%$ for objects with well-constrained stellar types. This is a substantial improvement over distance estimates derived from Gaia parallaxes alone and in line with results from previous work. We find the fits are able to accurately reproduce the de-reddened Gaia color-magnitude diagram, which serves as a useful consistency check of our results. We show that we are able to clearly detect large, kinematically-coherent substructures in our data 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.

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arXiv2025

Identification of 30,000 White Dwarf-Main Sequence binaries candidates from Gaia DR3 BP/RP(XP) low-resolution spectra

White dwarf-main sequence (WDMS) binary systems are essential probes for understanding binary stellar evolution and play a pivotal role in constraining theoretical models of various transient phenomena. In this study, we construct a catalog of WDMS binaries using Gaia DR3's low-resolution BP/RP (XP) spectra. Our approach integrates a model-independent neural network for spectral modelling with Gaussian Process Classification to accurately identify WDMS binaries among over 10 million stars within 1 kpc. This study identify approximately 30,000 WDMS binary candidates, including ~1,700 high-confidence systems confirmed through spectral fitting. Our technique is shown to be effective at detecting systems where the main-sequence star dominates the spectrum - cases that have historically challenged conventional methods. Validation using GALEX photometry reinforces the reliability of our classifications: 70\% of candidates with an absolute magnitude $M_{G} > 7$ exhibit UV excess, a characteristic signature of white dwarf companions. Our all-sky catalog of WDMS binaries expands the available dataset for studying binary evolution and white dwarf physics and sheds light on the formation of WDMS.

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arXiv2024

Dust extinction-curve variation in the translucent interstellar medium is driven by PAH growth

The first all-sky, high-resolution, 3D map of the optical extinction curve of the Milky Way (Zhang & Green 2024) revealed an unexpected steepening of the extinction curve in the moderate-density, "translucent" interstellar medium (ISM). We argue that this trend is driven by growth of polycyclic aromatic hydrocarbons (PAHs) through gas-phase accretion. We find a strong anti-correlation between the slope of the optical extinction curve -- parameterized by $R(V)$ -- and maps of PAH abundance -- parameterized by $q_{\rm PAH}$ -- derived from infrared emission. The range of observed $q_{\rm PAH}$ indicates PAH growth by a factor of $\sim$2 between $A_V \simeq 1$ and 3. This implies a factor-of-two stronger 2175 Angstrom feature, which is sufficient to lower $R(V)$ by the observed amount. This level of PAH growth is possible given rapid accretion timescales and the depletion of carbon in the translucent ISM. Spectral observations by JWST would provide a definitive test of this proposed explanation of $R(V)$ variation.

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arXiv2024

An Empirical Extinction Curve Revealed by Gaia XP Spectra and LAMOST

We present a direct measurement of extinction curves using corrected $Gaia$ XP spectra of the common sources in $Gaia$ DR3 and LAMOST DR7. Our analysis of approximately 370 thousand high-quality samples yielded a high-precision average extinction curve for the Milky Way. After incorporating infrared photometric data from 2MASS and WISE, the extinction curve spans wavelengths from 0.336 to 4.6 $μ$m. We determine an average $R_{55}$ of $2.730 \pm 0.007$, corresponding to $R_V= 3.073 \pm 0.009$, and a near-infrared power-law index $α$ of $1.935 \pm 0.037$. Our study confirmed some intermediate-scale structures within the optical range. Two new features were identified at 540 and 769 nm, and their intensities exhibited a correlation with extinction and $R_V$. This extinction curve can be used to investigate the characteristics of dust and enhance the extinction correction of Milky Way stars. A Python package for this extinction curve is available.

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arXiv2023

Recovering the gravitational potential in a rotating frame: Deep Potential applied to a simulated barred galaxy

Stellar kinematics provide a window into the gravitational field, and therefore into the distribution of all mass, including dark matter. Deep Potential is a method for determining the gravitational potential from a snapshot of stellar positions in phase space, using mathematical tools borrowed from deep learning to model the distribution function and solve the Collisionless Boltzmann Equation. In this work, we extend the Deep Potential method to rotating systems, and then demonstrate that it can accurately recover the gravitational potential, density distribution and pattern speed of a simulated barred disc galaxy, using only a frozen snapshot of the stellar velocities. We demonstrate that we are able to recover the bar pattern speed to within 15% in our simulated galaxy using stars in a 4 kpc sub-volume centered on a Solar-like position, and to within 20% in a 2 kpc sub-volume. In addition, by subtracting the mock "observed" stellar density from the recovered total density, we are able to infer the radial profile of the dark matter density in our simulated galaxy. This extension of Deep Potential is an important step in allowing its application to the Milky Way, which has rotating features, such as a central bar and spiral arms, and may moreover provide a new method of determining the pattern speed of the Milky Way bar.

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arXiv2023

Quantifying the influence of bars on action-based dynamical modelling of disc galaxies

Action-based dynamical modelling, using stars as dynamical tracers, is an excellent diagnostic to estimate the underlying axisymmetric matter distribution of the Milky Way. However, the Milky Way's bar causes non-axisymmetric resonance features in the stellar disc. Using Roadmapping (an action-based dynamical modelling framework to estimate the gravitational potential and the stellar distribution function), we systematically quantify the robustness of action-based modelling in the presence of a bar. We construct a set of test-particle simulations of barred galaxies (with varying bar properties), and apply Roadmapping to different survey volumes (with varying azimuthal position, size) drawn from these barred models. For realistic bar parameters, the global potential parameters are still recovered to within ~ 1 - 17 percent. However, with increasing bar strength, the best-fit values of the parameters progressively deviate from their true values. This happens due to a combination of radial heating, radial migration, and resonance overlap phenomena in our bar models. Furthermore, the azimuthal location and the size of the survey volumes play important roles in the successful recovery of the parameters. Survey volumes along the bar major axis produce larger (relative) errors in the best-fit parameter values. In addition, the potential parameters are better recovered for survey volumes with larger spatial coverage. As the Sun is located just ~ 28 - 33 degrees behind the bar's major axis, an estimate for the bar-induced systematic bias -- as provided by this study -- is therefore crucial for future modelling attempts of the Milky Way.

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arXiv2023

Parameters of 220 million stars from Gaia BP/RP spectra

We develop, validate and apply a forward model to estimate stellar atmospheric parameters ($T_{\rm eff}$, $\log{g}$ and $\mathrm{[Fe/H]}$), revised distances and extinctions for 220 million stars with XP spectra from $\textit{Gaia}$ DR3. Instead of using $\textit{ab initio}$ stellar models, we develop a data-driven model of $\textit{Gaia}$ XP spectra as a function of the stellar parameters, with a few straightforward built-in physical assumptions. We train our model on stellar atmospheric parameters from the LAMOST survey, which provides broad coverage of different spectral types. We model the $\textit{Gaia}$ XP spectra with all of their covariances, augmented by 2MASS and WISE photometry that greatly reduces degeneracies between stellar parameters, yielding more precise determinations of temperature and dust reddening. Taken together, our approach overcomes a number of important limitations that the astrophysical parameters released in $\textit{Gaia}$ DR3 faced, and exploits the full information content of the data. We provide the resulting catalog of stellar atmospheric parameters, revised parallaxes and extinction estimates, with all their uncertainties. The modeling procedure also produces an estimate of the optical extinction curve at the spectral resolution of the XP spectra ($R \sim 20-100$), which agrees reasonably well with the ${R(V) = 3.1}$ CCM model. Remaining limitations that will be addressed in future work are that the model assumes a universal extinction law, ignores binary stars and does not cover all parts of the Hertzsprung-Russell Diagram ($\textit{e.g.}$, white dwarfs).

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arXiv2023

Science with the Ultraviolet Explorer (UVEX)

UVEX is a proposed medium class Explorer mission designed to provide crucial missing capabilities that will address objectives central to a broad range of modern astrophysics. The UVEX design has two co-aligned wide-field imagers operating in the FUV and NUV and a powerful broadband medium resolution spectrometer. In its two-year baseline mission, UVEX will perform a multi-cadence synoptic all-sky survey 50/100 times deeper than GALEX in the NUV/FUV, cadenced surveys of the Large and Small Magellanic Clouds, rapid target of opportunity followup, as well as spectroscopic followup of samples of stars and galaxies. The science program is built around three pillars. First, UVEX will explore the low-mass, low-metallicity galaxy frontier through imaging and spectroscopic surveys that will probe key aspects of the evolution of galaxies by understanding how star formation and stellar evolution at low metallicities affect the growth and evolution of low-metallicity, low-mass galaxies in the local universe. Such galaxies contain half the mass in the local universe, and are analogs for the first galaxies, but observed at distances that make them accessible to detailed study. Second, UVEX will explore the dynamic universe through time-domain surveys and prompt spectroscopic followup capability will probe the environments, energetics, and emission processes in the early aftermaths of gravitational wave-discovered compact object mergers, discover hot, fast UV transients, and diagnose the early stages of stellar explosions. Finally, UVEX will become a key community resource by leaving a large all-sky legacy data set, enabling a wide range of scientific studies and filling a gap in the new generation of wide-field, sensitive optical and infrared surveys provided by the Rubin, Euclid, and Roman observatories. This paper discusses the scientific potential of UVEX, and the broad scientific program.

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arXiv2022

An empirical model of the Gaia DR3 selection function

Interpreting and modelling astronomical catalogues requires an understanding of the catalogues' completeness or selection function: objects of what properties had a chance to end up in the catalogue. Here we set out to empirically quantify the completeness of the overall Gaia DR3 catalogue. This task is not straightforward because Gaia is the all-sky optical survey with the highest angular resolution to date and no consistent ``ground truth'' exists to allow direct comparisons. However, well-characterised deeper imaging enables an empirical assessment of Gaia's $G$-band completeness across parts of the sky. On this basis, we devised a simple analytical completeness model of Gaia as a function of the observed $G$ magnitude and position over the sky, which accounts for both the effects of crowding and the complex Gaia scanning law. Our model only depends on a single quantity: the median magnitude $M_{10}$ in a patch of the sky of catalogued sources with $\texttt{astrometric_matched_transits}$ $\leq 10$. $M_{10}$ reflects elementary completeness decisions in the Gaia pipeline and is computable from the Gaia DR3 catalogue itself and therefore applicable across the whole sky. We calibrate our model using the Dark Energy Camera Plane Survey (DECaPS) and test its predictions against Hubble Space Telescope observations of globular clusters. We find that our model predicts Gaia's completeness values to a few per cent across the sky. We make the model available as a part of the $\texttt{gaiasf}$ Python package built and maintained by the GaiaUnlimited project: $\texttt{https://github.com/gaia-unlimited/gaiaunlimited}$

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