arXiv subjects

Gregory M. Green

Publications and source records attributed to Gregory M. Green.

34 records · Page 2Linked to original sources

arXiv2022

Deep Potential: Recovering the gravitational potential from a snapshot of phase space

One of the major goals of the field of Milky Way dynamics is to recover the gravitational potential field. Mapping the potential would allow us to determine the spatial distribution of matter - both baryonic and dark - throughout the Galaxy. We present a novel method for determining the gravitational field from a snapshot of the phase-space positions of stars, based only on minimal physical assumptions, which makes use of recently developed tools from the field of deep learning. We first train a normalizing flow on a sample of observed six-dimensional phase-space coordinates of stars, obtaining a smooth, differentiable approximation of the distribution function. Using the Collisionless Boltzmann Equation, we then find the gravitational potential - represented by a feed-forward neural network - that renders this distribution function stationary. This method, which we term "Deep Potential," is more flexible than previous parametric methods, which fit restricted classes of analytic models of the distribution function and potential to the data. We demonstrate Deep Potential on mock datasets, and demonstrate its robustness under various non-ideal conditions. Deep Potential is a promising approach to mapping the density of the Milky Way and other stellar systems, using rich datasets of stellar positions and kinematics now being provided by Gaia and ground-based spectroscopic surveys.

astro-ph.GA↗

arXiv2021

Data-Driven Stellar Models

We develop a data-driven model to map stellar parameters (effective temperature, surface gravity and metallicity) accurately and precisely to broad-band stellar photometry. This model must, and does, simultaneously constrain the passband-specific dust reddening vector in the Milky Way. 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 dataset with spectroscopic parameters from LAMOST, APOGEE and GALAH, Gaia parallaxes, and optical and near-infrared photometry from Gaia, Pan-STARRS~1, 2MASS and WISE. Testing the model on these datasets 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, stellar-temperature-dependent reddening vector. 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 may be obtained at https://doi.org/10.5281/zenodo.3902382.

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arXiv2020

Deep Potential: Recovering the gravitational potential from a snapshot of phase space

One of the major goals of the field of Milky Way dynamics is to recover the gravitational potential field. Mapping the potential would allow us to determine the spatial distribution of matter - both baryonic and dark - throughout the Galaxy. We present a novel method for determining the gravitational field from a snapshot of the phase-space positions of stars, based only on minimal physical assumptions. We first train a normalizing flow on a sample of observed phase-space positions, obtaining a smooth, differentiable approximation of the phase-space distribution function. Using the collisionless Boltzmann equation, we then find the gravitational potential - represented by a feed-forward neural network - that renders this distribution function stationary. This method is far more flexible than previous parametric methods, which fit narrow classes of analytic models to the data. This is a promising approach to uncovering the density structure of the Milky Way, using rich datasets of stellar kinematics that will soon become available.

astro-ph.GA↗

arXiv2020

Constraining the Distance to the North Polar Spur with Gaia DR2

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 toward latitudes ranging up to 55$^{\circ}$. First, we fit for the distance and extinction to stars toward the NPS using optical and near-infrared photometry and Gaia DR2 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 HI column density measures. We find that we can account for nearly 100% of the total column density of the NPS as lying within 140 pc for latitudes $>26^{\circ}$ and within 700 pc for latitudes $< 11^{\circ}$. 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 Sco-Cen association.

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arXiv2020

A Galactic-scale gas wave in the Solar Neighborhood

For the past 150 years, the prevailing view of the local Interstellar Medium (ISM) was based on a peculiarity known as the Gould's Belt, an expanding ring of young stars, gas, and dust, tilted about 20$^\circ$ to the Galactic plane. Still, the physical relation between local gas clouds has remained practically unknown because the distance accuracy to clouds is of the same order or larger than their sizes. With the advent of large photometric surveys and the Gaia satellite astrometric survey this situation has changed. Here we report the 3-D structure of all local cloud complexes. We find a narrow and coherent 2.7 kpc arrangement of dense gas in the Solar neighborhood 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, disputing the Gould Belt model. The new structure comprises the majority of nearby star-forming regions, has an aspect ratio of about 1:20, and contains about 3 million solar masses of gas. Remarkably, the new structure appears to be undulating and its 3-D distribution is well described by a damped sinusoidal wave on the plane of the Milky Way, with an average period of about 2 kpc and a maximum amplitude of about 160 pc. Our results represent a first step in the revision of the local gas distribution and Galactic structure and offer a new, broader context to studies on the transformation of molecular gas into stars.

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arXiv2020

A Compendium of Distances to Molecular Clouds in the Star Formation Handbook

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 which 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 $\sim5\%$. 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 $\lesssim10\%$ 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.

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arXiv2019

A 3D Dust Map Based on Gaia, Pan-STARRS 1 and 2MASS

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 declination of -30 degrees, out to a distance of several 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. 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. Our 3D dust map can be accessed at https://doi.org/10.7910/DVN/2EJ9TX or through the Python package "dustmaps," and can be queried interactively at http://argonaut.skymaps.info. Our catalog of stellar parameters can be accessed at https://doi.org/10.7910/DVN/AV9GXO.

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arXiv2019

A Large Catalog of Accurate Distances to Local Molecular Clouds: The Gaia DR2 Edition

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. (2014). First, we infer the distance and extinction to stars along sightlines towards 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 2-D 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. (2014) sightlines towards the Dame et al. (2001) and Magnani et al. (1985) 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 approximately 5-6%, though this can be higher for clouds at farther distances, due to the limitations of our single-cloud model.

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arXiv2019

Modeling the Connection Between Subhalos and Satellites in Milky Way-like Systems

We develop a comprehensive and flexible model for the connection between satellite galaxies and dark matter subhalos in dark matter-only zoom-in simulations of Milky Way (MW)--mass host halos. We systematically identify the physical and numerical uncertainties in the galaxy--halo connection and simulations underlying our method, including (i) the influence of host halo properties; (ii) the relationship between satellite luminosities and subhalo properties, including the effects of reionization; (iii) the relationship between satellite and subhalo locations; (iv) the relationship between satellite sizes and subhalo properties, including the effects of tidal stripping; (v) satellite and subhalo disruption due to baryonic effects; and (vi) artificial subhalo disruption and orphan satellites. To illustrate our approach, we fit this model to the luminosity distribution of both classical MW satellites and those discovered in the Sloan Digital Sky Survey by performing realistic mock observations that depend on the luminosity, size, and distance of our predicted satellites, and we infer the total satellite population that will be probed by upcoming surveys. We argue that galaxy size and surface brightness modeling will play a key role in interpreting current and future observations, as the expected number of observable satellites depends sensitively on their surface brightness distribution.

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arXiv2019

The unWISE Catalog: Two Billion Infrared Sources from Five Years of WISE Imaging

We present the unWISE Catalog, containing the positions and fluxes of roughly two billion objects observed by the Wide-field Infrared Survey Explorer (WISE) over the full sky. The unWISE Catalog has two advantages over the existing WISE catalog (AllWISE): first, it is based on significantly deeper imaging, and second, it features improved modeling of crowded regions. The deeper imaging used in the unWISE Catalog comes from the coaddition of all publicly available 3$-$5 micron WISE imaging, including that from the ongoing NEOWISE-Reactivation mission, thereby increasing the total exposure time by a factor of 5 relative to AllWISE. At these depths, even at high Galactic latitudes many sources are blended with their neighbors; accordingly, the unWISE analysis simultaneously fits thousands of sources to obtain accurate photometry. Our new catalog detects sources at 5-sigma roughly 0.7 magnitudes fainter than the AllWISE catalog and more accurately models millions of faint sources in the Galactic plane, enabling a wealth of Galactic and extragalactic science. In particular, relative to AllWISE, unWISE doubles the number of galaxies detected between redshifts 0 and 1 and triples the number between redshifts 1 and 2, cataloging more than half a billion galaxies over the whole sky.

astro-ph.IM↗

arXiv2018

Mapping Distances Across the Perseus Molecular Cloud Using CO Observations, Stellar Photometry, and Gaia DR2 Parallax Measurements

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 $\rm ^{12} CO$ 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 2MASS photometry, based on a technique presented in Green et al. 2014; Green et al. 2015 and implemented in their 3D "Bayestar" dust map of three-quarters of the sky. We then refine the Green et al. 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 towards 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 towards 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, IC348, B1, NGC1333, L1448, and L1451 star-forming regions and find that individual clouds are located between $\approx 275-300$ pc, with typical combined uncertainties of $\approx 5\%$. 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 $294\pm 17$ pc, about 60 pc higher 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.

astro-ph.GA↗

arXiv2018

A Color-locus Method for Mapping $R_V$ Using Ensembles of Stars

We present a simple but effective technique for measuring angular variation in $R_V$ across the sky. We divide stars from the Pan-STARRS1 catalog into Healpix pixels and determine the posterior distribution of reddening and $R_V$ for each pixel using two independent Monte Carlo methods. We find the two methods to be self-consistent in the limits where they are expected to perform similarly. We also find some agreement with high-precision photometric studies of $R_V$ in Perseus and Ophiuchus, as well as with a map of reddening near the Galactic plane based on stellar spectra from APOGEE. While current studies of $R_V$ are mostly limited to isolated clouds, we have developed a systematic method for comparing $R_V$ values for the majority of observable dust. This is a proof of concept for a more rigorous Galactic reddening map.x

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arXiv2018

Galactic Reddening in 3D from Stellar Photometry - An Improved Map

We present a new 3D map of interstellar dust reddening, covering three quarters of the sky (declinations greater than -30 degrees) out to a distance of several kiloparsecs. The map is based on high-quality stellar photometry of 800 million stars from Pan-STARRS 1 and 2MASS. We divide the sky into sightlines containing a few hundred stars each, and then infer stellar distances and types, along with the line-of-sight dust distribution. Our new map incorporates a more accurate average extinction law and an additional 1.5 years of Pan-STARRS 1 data, tracing dust to greater extinctions and at higher angular resolutions than our previous map. Out of the plane of the Galaxy, our map agrees well with 2D reddening maps derived from far-infrared dust emission. After accounting for a 15% difference in scale, we find a mean scatter of 10% between our map and the Planck far-infrared emission-based dust map, out to a depth of 0.8 mag in E(r-z), with the level of agreement varying over the sky. Our map can be downloaded at http://argonaut.skymaps.info, or by its DOI: 10.7910/DVN/LCYHJG.

astro-ph.GA↗

arXiv2016

On Galactic density modeling in the presence of dust extinction

Inferences about the spatial density or phase-space structure of stellar populations in the Milky Way require a precise determination of the effective survey volume. The volume observed by surveys such as Gaia or near-infrared spectroscopic surveys, which have good coverage of the Galactic mid-plane region, is highly complex because of the abundant small-scale structure in the three-dimensional interstellar dust extinction. We introduce a novel framework for analyzing the importance of small-scale structure in the extinction. This formalism demonstrates that the spatially-complex effect of extinction on the selection function of a pencil-beam or contiguous sky survey is equivalent to a low-pass filtering of the extinction-affected selection function with the smooth density field. We find that the angular resolution of current 3D extinction maps is sufficient for analyzing Gaia sub-samples of millions of stars. However, the current distance resolution is inadequate and needs to be improved by an order of magnitude, especially in the inner Galaxy. We also present a practical and efficient method for properly taking the effect of extinction into account in analyses of Galactic structure through an effective selection function. We illustrate its use with the selection function of red-clump stars in APOGEE using and comparing a variety of current 3D extinction maps.

astro-ph.GA↗

arXiv2015

Constructing A Flexible Likelihood Function For Spectroscopic Inference

We present a modular, extensible likelihood framework for spectroscopic inference based on synthetic model spectra. The subtraction of an imperfect model from a continuously sampled spectrum introduces covariance between adjacent datapoints (pixels) into the residual spectrum. For the high signal-to-noise data with large spectral range that is commonly employed in stellar astrophysics, that covariant structure can lead to dramatically underestimated parameter uncertainties (and, in some cases, biases). We construct a likelihood function that accounts for the structure of the covariance matrix, utilizing the machinery of Gaussian process kernels. This framework specifically address the common problem of mismatches in model spectral line strengths (with respect to data) due to intrinsic model imperfections (e.g., in the atomic/molecular databases or opacity prescriptions) by developing a novel local covariance kernel formalism that identifies and self-consistently downweights pathological spectral line "outliers." By fitting many spectra in a hierarchical manner, these local kernels provide a mechanism to learn about and build data-driven corrections to synthetic spectral libraries. An open-source software implementation of this approach is available at http://iancze.github.io/Starfish, including a sophisticated probabilistic scheme for spectral interpolation when using model libraries that are sparsely sampled in the stellar parameters. We demonstrate some salient features of the framework by fitting the high resolution $V$-band spectrum of WASP-14, an F5 dwarf with a transiting exoplanet, and the moderate resolution $K$-band spectrum of Gliese 51, an M5 field dwarf.

astro-ph.SR↗

arXiv2015

A Three-Dimensional Map of Milky-Way Dust

We present a three-dimensional map of interstellar dust reddening, covering three-quarters of the sky out to a distance of several kiloparsecs, based on Pan-STARRS 1 and 2MASS photometry. The map reveals a wealth of detailed structure, from filaments to large cloud complexes. The map has a hybrid angular resolution, with most of the map at an angular resolution of 3.4' to 13.7', and a maximum distance resolution of ~25%. The three-dimensional distribution of dust is determined in a fully probabilistic framework, yielding the uncertainty in the reddening distribution along each line of sight, as well as stellar distances, reddenings and classifications for 800 million stars detected by Pan-STARRS 1. We demonstrate the consistency of our reddening estimates with those of two-dimensional emission-based maps of dust reddening. In particular, we find agreement with the Planck 353 GHz optical depth-based reddening map to within 0.05 mag in E(B-V) to a depth of 0.5 mag, and explore systematics at reddenings less than E(B-V) ~ 0.08 mag. We validate our per-star reddening estimates by comparison with reddening estimates for stars with both SDSS photometry and SEGUE spectral classifications, finding per-star agreement to within 0.1 mag out to a stellar E(B-V) of 1 mag. We compare our map to two existing three-dimensional dust maps, by Marshall et al. (2006) and Lallement et al. (2013), demonstrating our finer angular resolution, and better distance resolution compared to the former within ~3 kpc. The map can be queried or downloaded at http://argonaut.skymaps.info. We expect the three-dimensional reddening map presented here to find a wide range of uses, among them correcting for reddening and extinction for objects embedded in the plane of the Galaxy, studies of Galactic structure, calibration of future emission-based dust maps and determining distances to objects of known reddening.

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