Showing 1–50 of 187 results for author: Finkbeiner, D

Unraveling Chemical Enrichment in Extreme Emission-Line Galaxies: A Multi-Element Bayesian View of Bursty Star Formation and Galaxy Evolution in DESI

Authors: Razieh Emami, James A. A. Trussler, Tiger Yu-Yang Hsiao, Kaley Brauer, Lars Hernquist, Randall Smith, Douglas Finkbeiner, Fengwu Sun, Rebecca Davies, James F. Steiner, Mark Vogelsberger, Tobias Looser, Grant Tremblay, Letizia Bugiani

Abstract: Extreme emission-line galaxies (EELGs) probe chemical enrichment in low-mass, bursty systems where star formation, feedback, and gas accretion are poorly constrained. Using DESI DR1, we select 23 nearby EELGs with detections of 19 ionic species (S/N $\geq$ 4), stellar masses $ M_* \geq 10^7 M_{\odot}$, and extreme H$α$ and [O III] 5007 equivalent widths (EW $\geq$ 500 Angstrom). We infer non-param… ▽ More Extreme emission-line galaxies (EELGs) probe chemical enrichment in low-mass, bursty systems where star formation, feedback, and gas accretion are poorly constrained. Using DESI DR1, we select 23 nearby EELGs with detections of 19 ionic species (S/N $\geq$ 4), stellar masses $ M_* \geq 10^7 M_{\odot}$, and extreme H$α$ and [O III] 5007 equivalent widths (EW $\geq$ 500 Angstrom). We infer non-parametric star-formation histories and fit a Bayesian single-zone chemical-evolution model to O, N, Ne, S, and Ar, allowing time-dependent star-formation efficiency, outflow mass loading, and evolving inflow metallicity. We find short depletion timescales and large mass-loading factors, indicating rapid gas cycling in a burst-driven, non-equilibrium regime, with depletion times below Kennicutt-Schmidt expectations. Star-formation efficiency and outflows are well constrained, while inflow metallicity is weaker due to degeneracies with metal production. Abundance ratios isolate physical drivers: star-formation efficiency sets evolutionary tracks, outflows regulate metal retention and X/O normalization, and inflow metallicity sets baseline enrichment. N/O strongly constrains burst timing and gas flows, Ne/O remains nearly invariant, and S/O and Ar/O show intermediate sensitivity. These results demonstrate that multi-element abundances provide a direct probe of baryon-cycle processes in extreme low-mass starbursts. △ Less

Submitted 21 April, 2026; originally announced April 2026.

Comments: 25 pages, 12 pages

Effect of local environment on Ly$α$ line profile in DESI/ODIN LAEs

Authors: Ana Sofía M. Uzsoy, Arjun Dey, Anand Raichoor, Douglas P. Finkbeiner, Vandana Ramakrishnan, Kyoung-Soo Lee, Eric Gawiser, Jessica Nicole Aguilar, Steven Ahlen, Abhijeet Anand, Davide Bianchi, David Brooks, Todd Claybaugh, Axel de la Macorra, Peter Doel, Simone Ferraro, Nicole M. Firestone, Andreu Font-Ribera, Jaime E. Forero-Romero, Enrique Gaztañaga, Lucia Guaita, Gaston Gutierrez, Hiram K. Herrera-Alcantar, Ho Seong Hwang, Mustapha Ishak , et al. (29 additional authors not shown)

Abstract: Lyman-Alpha Emitters (LAEs) are star-forming galaxies with significant Ly$α$ emission and are often used as tracers of large-scale structure at high redshift. We explore the relationship between the Ly$α$ line profile and environmental density with spectroscopy from the Dark Energy Spectroscopic Instrument (DESI) of LAEs selected with narrow-band photometry through the One-hundred-deg$^2$ DECam Im… ▽ More Lyman-Alpha Emitters (LAEs) are star-forming galaxies with significant Ly$α$ emission and are often used as tracers of large-scale structure at high redshift. We explore the relationship between the Ly$α$ line profile and environmental density with spectroscopy from the Dark Energy Spectroscopic Instrument (DESI) of LAEs selected with narrow-band photometry through the One-hundred-deg$^2$ DECam Imaging in Narrowbands (ODIN) survey. We use LAE surface density maps in the N419 (z $\sim$ 2.45) and N501 (z $\sim$ 3.12) narrow bands to probe the relationship between local environmental density and the Ly$α$ line profile. In both narrow bands, we stack the LAE spectra in bins of environmental density and inside and outside of protocluster regions. The N501 data shows $\sim$15% higher Ly$α$ line luminosity for galaxies in protoclusters, suggesting increased star formation in these regions. However, the line luminosity is not appreciably greater in protocluster galaxies in the N419 band, suggesting a potential redshift evolution of this effect. The shape of the line profile itself does not vary with environmental density, suggesting that line shape changes are caused by local effects independent of a galaxy's environment. These data indicate a potential relationship between LAE local environmental density, ionized gas distribution, and Ly$α$ line luminosity. △ Less

Submitted 21 November, 2025; originally announced November 2025.

Comments: 17 pages, 10 + 6 figures

Data-Driven Stellar Spectral Modelling with GSPICE

Authors: Douglas P. Finkbeiner, Joshua S. Speagle, Tanveer Karim

Abstract: 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 co… ▽ More 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. △ Less

Submitted 20 November, 2025; originally announced November 2025.

Comments: 18 pages, 12 figures

Modeling X-ray photon pile-up with a normalizing flow

Authors: Ole König, Daniela Huppenkothen, Douglas Finkbeiner, Christian Kirsch, Jörn Wilms, Justina R. Yang, James F. Steiner, Juan Rafael Martínez-Galarza

Abstract: The dynamic range of imaging detectors flown on-board X-ray observatories often only covers a limited flux range of extrasolar X-ray sources. The analysis of bright X-ray sources is complicated by so-called pile-up, which results from high incident photon flux. This nonlinear effect distorts the measured spectrum, resulting in biases in the inferred physical parameters, and can even lead to a comp… ▽ More The dynamic range of imaging detectors flown on-board X-ray observatories often only covers a limited flux range of extrasolar X-ray sources. The analysis of bright X-ray sources is complicated by so-called pile-up, which results from high incident photon flux. This nonlinear effect distorts the measured spectrum, resulting in biases in the inferred physical parameters, and can even lead to a complete signal loss in extreme cases. Piled-up data are commonly discarded due to resulting intractability of the likelihood. As a result, a large number of archival observations remain underexplored. We present a machine learning solution to this problem, using a simulation-based inference framework that allows us to estimate posterior distributions of physical source parameters from piled-up eROSITA data. We show that a normalizing flow produces better-constrained posterior densities than traditional mitigation techniques, as more data can be leveraged. We consider model- and calibration-dependent uncertainties and the applicability of such an algorithm to real data in the eROSITA archive. △ Less

Submitted 14 November, 2025; originally announced November 2025.

Comments: Accepted in Machine Learning and the Physical Sciences Workshop, NeurIPS 2025

Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy

Authors: Manuel Perez-Carrasco, Maya Nasr, Sebastien Roche, Chris Chan Miller, Zhan Zhang, Core Francisco Park, Eleanor Walker, Cecilia Garraffo, Douglas Finkbeiner, Sasha Ayvazov, Jonathan Franklin, Bingkun Luo, Xiong Liu, Ritesh Gautam, Steven Wofsy

Abstract: Effective cloud and cloud shadow detection is a critical prerequisite for accurate retrieval of concentrations of atmospheric methane (CH4) or other trace gases in hyperspectral remote sensing. This challenge is especially pertinent for MethaneSAT, a satellite mission launched in March 2024, to fill a significant data gap in terms of resolution, precision and swath between coarse-resolution global… ▽ More Effective cloud and cloud shadow detection is a critical prerequisite for accurate retrieval of concentrations of atmospheric methane (CH4) or other trace gases in hyperspectral remote sensing. This challenge is especially pertinent for MethaneSAT, a satellite mission launched in March 2024, to fill a significant data gap in terms of resolution, precision and swath between coarse-resolution global mappers and fine-scale point-source imagers of methane, and for its airborne companion mission, MethaneAIR. MethaneSAT delivers hyperspectral data at an intermediate spatial resolution (approx. 100 x 400, m), whereas MethaneAIR provides even finer resolution (approx. 25 m), enabling the development of highly detailed maps of concentrations that enable quantification of both the sources and rates of emissions. In this study, we use machine learning methods to address the cloud and cloud shadow detection problem for sensors with these high spatial resolutions. Cloud and cloud shadows in remote sensing data need to be effectively screened out as they bias methane retrievals in remote sensing imagery and impact the quantification of emissions. We deploy and evaluate conventional techniques-including Iterative Logistic Regression (ILR) and Multilayer Perceptron (MLP)-with advanced deep learning architectures, namely U-Net and a Spectral Channel Attention Network (SCAN) method. Our results show that conventional methods struggle with spatial coherence and boundary definition, affecting the detection of clouds and cloud shadows. Deep learning models substantially improve detection quality: U-Net performs best in preserving spatial structure, while SCAN excels at capturing fine boundary details... Our data and code is publicly available at: https://doi.org/10.7910/DVN/IKLZOJ △ Less

Submitted 14 February, 2026; v1 submitted 23 September, 2025; originally announced September 2025.

Journal ref: IEEE Transactions on Geoscience and Remote Sensing 2026

The Nineteenth Data Release of the Sloan Digital Sky Survey

Authors: SDSS Collaboration, Gautham Adamane Pallathadka, Mojgan Aghakhanloo, James Aird, Andrés Almeida, Singh Amrita, Friedrich Anders, Scott F. Anderson, Stefan Arseneau, Consuelo González Avila, Shir Aviram, Catarina Aydar, Carles Badenes, Jorge K. Barrera-Ballesteros, Franz E. Bauer, Aida Behmard, Michelle Berg, F. Besser, Christian Moni Bidin, Dmitry Bizyaev, Guillermo Blanc, Michael R. Blanton, Jo Bovy, William Nielsen Brandt, Joel R. Brownstein , et al. (187 additional authors not shown)

Abstract: Mapping the local and distant Universe is key to our understanding of it. For decades, the Sloan Digital Sky Survey (SDSS) has made a concerted effort to map millions of celestial objects to constrain the physical processes that govern our Universe. The most recent and fifth generation of SDSS (SDSS-V) is organized into three scientific ``mappers". Milky Way Mapper (MWM) that aims to chart the var… ▽ More Mapping the local and distant Universe is key to our understanding of it. For decades, the Sloan Digital Sky Survey (SDSS) has made a concerted effort to map millions of celestial objects to constrain the physical processes that govern our Universe. The most recent and fifth generation of SDSS (SDSS-V) is organized into three scientific ``mappers". Milky Way Mapper (MWM) that aims to chart the various components of the Milky Way and constrain its formation and assembly, Black Hole Mapper (BHM), which focuses on understanding supermassive black holes in distant galaxies across the Universe, and Local Volume Mapper (LVM), which uses integral field spectroscopy to map the ionized interstellar medium in the local group. This paper describes and outlines the scope and content for the nineteenth data release (DR19) of SDSS and the most substantial to date in SDSS-V. DR19 is the first to contain data from all three mappers. Additionally, we also describe nine value added catalogs (VACs) that enhance the science that can be conducted with the SDSS-V data. Finally, we discuss how to access SDSS DR19 and provide illustrative examples and tutorials. △ Less

Submitted 9 July, 2025; originally announced July 2025.

Comments: Submitted to AASJournals. 56 Pages, 9 Tables, 11 Figures

The Roman View of Strong Gravitational Lenses

Authors: Bryce Wedig, Tansu Daylan, Simon Birrer, Francis-Yan Cyr-Racine, Cora Dvorkin, Douglas P. Finkbeiner, Alan Huang, Xiaosheng Huang, Rahul Karthik, Narayan Khadka, Priyamvada Natarajan, Anna M. Nierenberg, Annika H. G. Peter, Justin D. R. Pierel, Xianzhe TZ Tang, Risa H. Wechsler

Abstract: Galaxy-galaxy strong gravitational lenses can constrain dark matter models and the Lambda Cold Dark Matter cosmological paradigm at sub-galactic scales. Currently, there is a dearth of images of these rare systems with high signal-to-noise and angular resolution. The Nancy Grace Roman Space Telescope (hereafter, Roman), scheduled for launch in late 2026, will play a transformative role in strong l… ▽ More Galaxy-galaxy strong gravitational lenses can constrain dark matter models and the Lambda Cold Dark Matter cosmological paradigm at sub-galactic scales. Currently, there is a dearth of images of these rare systems with high signal-to-noise and angular resolution. The Nancy Grace Roman Space Telescope (hereafter, Roman), scheduled for launch in late 2026, will play a transformative role in strong lensing science with its planned wide-field surveys. With its remarkable 0.281 square degree field of view and diffraction-limited angular resolution of ~0.1 arcsec, Roman is uniquely suited to characterizing dark matter substructure from a robust population of strong lenses. We present a yield simulation of detectable strong lenses in Roman's planned High Latitude Wide Area Survey (HLWAS). We simulate a population of galaxy-galaxy strong lenses across cosmic time with Cold Dark Matter subhalo populations, select those detectable in the HLWAS, and generate simulated images accounting for realistic Wide Field Instrument detector effects. For a fiducial case of single 146-second exposures, we predict around 160,000 detectable strong lenses in the HLWAS, of which about 500 will have sufficient signal-to-noise to be amenable to detailed substructure characterization. We investigate the effect of the variation of the point-spread function across Roman's field of view on detecting individual subhalos and the suppression of the subhalo mass function at low masses. Our simulation products are available to support strong lens science with Roman, such as training neural networks and validating dark matter substructure analysis pipelines. △ Less

Submitted 3 June, 2025; originally announced June 2025.

Comments: 23 pages, 12 figures, accepted for publication in The Astrophysical Journal, code at https://github.com/AstroMusers/mejiro, data at https://doi.org/10.5281/zenodo.14216839

Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts and Photometric Targeting

Authors: Ana Sofía M. Uzsoy, Andrew K. Saydjari, Arjun Dey, Anand Raichoor, Douglas P. Finkbeiner, Eric Gawiser, Kyoung-Soo Lee, Steven Ahlen, Davide Bianchi, David Brooks, Todd Claybaugh, Andrei Cuceu, Axel de la Macorra, Peter Doel, Andreu Font-Ribera, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Gaston Gutierrez, Mustapha Ishak, Robert Kehoe, David Kirkby, Anthony Kremin, Martin Landriau, Laurent Le Guillou , et al. (15 additional authors not shown)

Abstract: Lyman Alpha Emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrow-band photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automa… ▽ More Lyman Alpha Emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrow-band photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automatically determine spectroscopic redshifts for LAEs while marginalizing over sky residuals. We use visually inspected spectra of LAEs obtained using the Dark Energy Spectroscopic Instrument (DESI) to create a data-driven prior and can determine redshift by jointly inferring sky residual, LAE, and residual components for each individual spectrum. We demonstrate this method on 881 spectroscopically observed $z = 2-4$ DESI LAE candidate spectra and determine their redshifts with $>$90% accuracy when validated against visually inspected redshifts. Using the $Δχ^2$ value from our pipeline as a proxy for detection confidence, we then explore potential survey design choices and implications for targeting LAEs with medium-band photometry. This method allows for scalability and accuracy in determining redshifts from DESI spectra, and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys. △ Less

Submitted 25 March, 2026; v1 submitted 9 April, 2025; originally announced April 2025.

Comments: 20 pages, 11 figures

Novel Polarimetric Analysis of Near Horizon Flaring Episodes in M87* in Millimeter Wavelength

Authors: Razieh Emami, Matthew Liska, Koushik Chatterjee, Geoffrey C. Bower, Wystan Benbow, Douglas Finkbeiner, Maciek Wielgus, Lars Hernquist, Randall Smith, Grant Tremblay, Angelo Ricarte, James F. Steiner, Avery E. Broderick, Saurabh, Jordy Davelaar, Josh Grindlay, Mark Vogelsberger, Chi-Kwan Chan

Abstract: Recent multi-wavelength observations of M87* \citep{2024A&A...692A.140A} revealed a high-energy $γ$-ray flare without a corresponding millimeter counterpart. We present a theoretical polarimetric study to evaluate the presence and nature of a potential millimeter flare in M87*, using a suite of general relativistic magnetohydrodynamical simulations with varying black hole (BH) spins and magnetic f… ▽ More Recent multi-wavelength observations of M87* \citep{2024A&A...692A.140A} revealed a high-energy $γ$-ray flare without a corresponding millimeter counterpart. We present a theoretical polarimetric study to evaluate the presence and nature of a potential millimeter flare in M87*, using a suite of general relativistic magnetohydrodynamical simulations with varying black hole (BH) spins and magnetic field configurations. We find that the emergence of a millimeter flare is strongly influenced by both spin and magnetic structure, with limited sensitivity to the electron distribution (thermal vs. non-thermal). We model the intensity light curve with a damped random walk (DRW) and compare the characteristic timescale ($τ$) with recent SMA observations, finding that the simulated $τ$ exceeds observed values by over an order of magnitude. In a flaring case with BH spin a=+0.5, we identify a distinct millimeter flare followed by an order-of-magnitude flux drop. All Stokes parameters show variability near the flare, including a sign reversal in the electric vector position angle. While most $β_m$ modes remain stable, the $EB$-correlation phase is highly sensitive to both the flare peak and decay. We examine polarimetric signatures in photon sub-rings, focusing on modes ns=0 and ns=1. The ns=0 signal closely matches the full image, while ns=1 reveals distinct behaviors, highlighting the potential of space VLBI to isolate sub-ring features. Finally, we analyze the magnetic and velocity field evolution during the flare, finding that magnetic reconnection weakens during the flux decay, and the clockwise velocity flow transitions into an outflow-dominated regime. These results suggest that transient radio variability near flares encodes key information about black hole spin and magnetic field structure, offering a novel probe into the physics of active galactic nuclei. △ Less

Submitted 14 April, 2025; v1 submitted 6 April, 2025; originally announced April 2025.

Comments: 24 pages, 23 figures

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

Authors: Catherine Zucker, Andrew K. Saydjari, Joshua S. Speagle, Edward F. Schlafly, Gregory M. Green, Robert Benjamin, Joshua Peek, Gordian Edenhofer, Alyssa Goodman, Michael A. Kuhn, Douglas P. Finkbeiner

Abstract: 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 us… ▽ More 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. △ Less

Submitted 19 August, 2025; v1 submitted 4 March, 2025; originally announced March 2025.

Comments: Accepted for publication in ApJ. 34 pages, 20 figures. Equal contribution of first three authors (Zucker, Saydjari, & Speagle). Easily access the map via the dustmaps python package (https://dustmaps.readthedocs.io/en/latest/)

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

Authors: Joshua S. Speagle, Catherine Zucker, Angus Beane, Phillip A. Cargile, Aaron Dotter, Douglas P. Finkbeiner, Gregory M. Green, Benjamin D. Johnson, Edward F. Schlafly, Ana Bonaca, Charlie Conroy, Gwendolyn Eadie, Daniel J. Eisenstein, Alyssa A. Goodman, Jiwon Jesse Han, Harshil M. Kamdar, Rohan Naidu, Hans-Walter Rix, Andrew K. Saydjari, Yuan-Sen Ting, Ioana A. Zelko

Abstract: 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 (… ▽ More 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. △ Less

Submitted 3 March, 2025; originally announced March 2025.

Comments: 33 pages, 17 figures main text (+23 pages, +10 figures in appendices). To be re-submitted to AAS Journals. Package documentation to be added in coming weeks (as of 2025-03-03)

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

Authors: Joshua S. Speagle, Catherine Zucker, Ana Bonaca, Phillip A. Cargile, Benjamin D. Johnson, Angus Beane, Charlie Conroy, Douglas P. Finkbeiner, Gregory M. Green, Harshil M. Kamdar, Rohan Naidu, Hans-Walter Rix, Edward F. Schlafly, Aaron Dotter, Gwendolyn Eadie, Daniel J. Eisenstein, Alyssa A. Goodman, Jiwon Jesse Han, Andrew K. Saydjari, Yuan-Sen Ting, Ioana A. Zelko

Abstract: 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 fin… ▽ More 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. △ Less

Submitted 3 March, 2025; originally announced March 2025.

Comments: Published in ApJ in 2024. 31 pages, 17 figures

Journal ref: The Astrophysical Journal, Volume 970, Issue 2, id.121, 22 pp (2024)

A Fast Periodicity Detection Algorithm Sensitive to Arbitrary Waveforms

Authors: Douglas P. Finkbeiner, Thomas A. Prince, Samuel E. Whitebook

Abstract: A reexamination of period finding algorithms is prompted by new large area astronomical sky surveys that can identify billions of individual sources having a thousand or more observations per source. This large increase in data necessitates fast and efficient period detection algorithms. In this paper, we provide an initial description of an algorithm that is being used for detection of periodic b… ▽ More A reexamination of period finding algorithms is prompted by new large area astronomical sky surveys that can identify billions of individual sources having a thousand or more observations per source. This large increase in data necessitates fast and efficient period detection algorithms. In this paper, we provide an initial description of an algorithm that is being used for detection of periodic behavior in a sample of 1.5 billion objects using light curves generated from Zwicky Transient Facility (ZTF) data (Bellm et al. 2019; Masci et al. 2018). We call this algorithm "Fast Periodicity Weighting" (FPW), derived using a Gaussian Process (GP) formalism. A major advantage of the FPW algorithm for ZTF analysis is that it is agnostic to the details of the phase-folded waveform. Periodic sources in ZTF show a wide variety of waveforms, some quite complex, including eclipsing objects, sinusoidally varying objects also exhibiting eclipses, objects with cyclotron emission at various phases, and accreting objects with complex waveforms. We describe the FPW algorithm and its application to ZTF, and provide efficient code for both CPU and GPU. △ Less

Submitted 31 January, 2025; originally announced February 2025.

Comments: 17 pages, 7 figures, code attached

Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m/s RV Precision for APOGEE in the Plate Era

Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton

Abstract: The radial velocity catalog from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) is unique in its simultaneously large volume and high precision as a result of its decade-long survey duration, multiplexing (600 fibers), and spectral resolution of $R \sim 22,500$. However, previous data reductions of APOGEE have not fully realized the potential radial velocity (RV) precision of… ▽ More The radial velocity catalog from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) is unique in its simultaneously large volume and high precision as a result of its decade-long survey duration, multiplexing (600 fibers), and spectral resolution of $R \sim 22,500$. However, previous data reductions of APOGEE have not fully realized the potential radial velocity (RV) precision of the instrument. Here we present an RV catalog based on a new reduction of all 2.6 million visits of APOGEE DR17 and validate it against improved estimates for the theoretical RV performance. The core ideas of the new reduction are the simultaneous modeling of all components in the spectra, rather than a separate subtraction of point estimates for the sky, and a marginalization over stellar types, rather than a grid search for an optimum. We show that this catalog, when restricted to RVs measured with the same fiber, achieves noise-limited precision down to 30 m/s and delivers well-calibrated uncertainties. We also introduce a general method for calibrating fiber-to-fiber constant RV offsets and demonstrate its importance for high RV precision work in multi-fiber spectrographs. After calibration, we achieve 47 m/s RV precision on the combined catalog with RVs measured with different fibers. This degradation in precision relative to measurements with only a single fiber suggests that refining line spread function models should be a focus in SDSS-V to improve the fiber-unified RV catalog. △ Less

Submitted 13 August, 2024; originally announced August 2024.

Comments: 22 pages, 13 figures, SDSS-V Technical Paper, Submitted to AJ

Correcting Turbulence-induced Errors in Fiber Positioning for the Dark Energy Spectroscopic Instrument

Authors: E. F. Schlafly, J. Guy, K. Honscheid, S. Kent, S. E. Koposov, J. Aguilar, S. Ahlen, S. Bailey, D. Brooks, T. Claybaugh, K. Dawson, P. Doel, K. Fanning, D. P. Finkbeiner, A. Font-Ribera, J. E. Forero-Romero, S. Gontcho A Gontcho, G. Gutierrez, D. Kirkby, T. Kisner, A. Kremin, J. Lasker, M. Landriau, L. Le Guillou, M. E. Levi , et al. (15 additional authors not shown)

Abstract: Highly-multiplexed, robotic, fiber-fed spectroscopic surveys are observing tens of millions of stars and galaxies. For many systems, accurate positioning relies on imaging the fibers in the focal plane and feeding that information back to the robotic positioners to correct their positions. Inhomogeneities and turbulence in the air between the focal plane and the imaging camera can affect the measu… ▽ More Highly-multiplexed, robotic, fiber-fed spectroscopic surveys are observing tens of millions of stars and galaxies. For many systems, accurate positioning relies on imaging the fibers in the focal plane and feeding that information back to the robotic positioners to correct their positions. Inhomogeneities and turbulence in the air between the focal plane and the imaging camera can affect the measured positions of fibers, limiting the accuracy with which fibers can be placed on targets. For the Dark Energy Spectroscopic Instrument, we dramatically reduced the effect of turbulence on measurements of positioner locations in the focal plane by taking advantage of stationary positioners and the correlation function of the turbulence. We were able to reduce positioning errors from 7.3 microns to 3.5 microns, speeding the survey by 1.6% under typical conditions. △ Less

Submitted 3 February, 2025; v1 submitted 10 July, 2024; originally announced July 2024.

Comments: 11 pages, 5 figures. Update to author's version of published version

Unravelling the role of merger histories in the population of In situ stars: linking IllustrisTNG cosmological simulation to H3 survey

Authors: Razieh Emami, Lars Hernquist, Randall Smith, James F. Steiner, Grant Tremblay, Douglas Finkbeiner, Mark Vogelsberger, Josh Grindlay, Federico Marinacci, Kung-Yi Su, Cecilia Garraffo, Yuan-Sen Ting, Phillip A. Cargile, Rebecca L. Davies, Chloë E. Benton, Yijia Li, Letizia Bugiani, Amir H. Khoram, Sownak Bose

Abstract: We undertake a comprehensive investigation into the distribution of in~situ stars within Milky Way-like galaxies, leveraging TNG50 simulations and comparing their predictions with data from the H3 survey. Our analysis reveals that 28% of galaxies demonstrate reasonable agreement with H3, while only 12% exhibit excellent alignment in their profiles, regardless of the specific spatial cut employed t… ▽ More We undertake a comprehensive investigation into the distribution of in~situ stars within Milky Way-like galaxies, leveraging TNG50 simulations and comparing their predictions with data from the H3 survey. Our analysis reveals that 28% of galaxies demonstrate reasonable agreement with H3, while only 12% exhibit excellent alignment in their profiles, regardless of the specific spatial cut employed to define in~situ stars. To uncover the underlying factors contributing to deviations between TNG50 and H3 distributions, we scrutinise correlation coefficients among internal drivers(e.g., virial radius, star formation rate [SFR]) and merger-related parameters (such as the effective mass-ratio, mean distance, average redshift, total number of mergers, average spin-ratio and maximum spin alignment between merging galaxies). Notably, we identify significant correlations between deviations from observational data and key parameters such as the median slope of virial radius, mean SFR values, and the rate of SFR change across different redshift scans. Furthermore, positive correlations emerge between deviations from observational data and parameters related to galaxy mergers. We validate these correlations using the Random Forest Regression method. Our findings underscore the invaluable insights provided by the H3 survey in unravelling the cosmic history of galaxies akin to the Milky Way, thereby advancing our understanding of galactic evolution and shedding light on the formation and evolution of Milky Way-like galaxies in cosmological simulations. △ Less

Submitted 18 April, 2025; v1 submitted 9 July, 2024; originally announced July 2024.

Comments: 22 pages, 13 figures

Journal ref: Publ. Astron. Soc. Aust. 42 (2025) e082

Diffusion-HMC: Parameter Inference with Diffusion-model-driven Hamiltonian Monte Carlo

Authors: Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner

Abstract: Diffusion generative models have excelled at diverse image generation and reconstruction tasks across fields. A less explored avenue is their application to discriminative tasks involving regression or classification problems. The cornerstone of modern cosmology is the ability to generate predictions for observed astrophysical fields from theory and constrain physical models from observations usin… ▽ More Diffusion generative models have excelled at diverse image generation and reconstruction tasks across fields. A less explored avenue is their application to discriminative tasks involving regression or classification problems. The cornerstone of modern cosmology is the ability to generate predictions for observed astrophysical fields from theory and constrain physical models from observations using these predictions. This work uses a single diffusion generative model to address these interlinked objectives -- as a surrogate model or emulator for cold dark matter density fields conditional on input cosmological parameters, and as a parameter inference model that solves the inverse problem of constraining the cosmological parameters of an input field. The model is able to emulate fields with summary statistics consistent with those of the simulated target distribution. We then leverage the approximate likelihood of the diffusion generative model to derive tight constraints on cosmology by using the Hamiltonian Monte Carlo method to sample the posterior on cosmological parameters for a given test image. Finally, we demonstrate that this parameter inference approach is more robust to small perturbations of noise to the field than baseline parameter inference networks. △ Less

Submitted 25 February, 2025; v1 submitted 8 May, 2024; originally announced May 2024.

Comments: Published in ApJ, Updated with the accepted version

Journal ref: The Astrophysical Journal, 978:64 (14pp), 2025

Hyperspectral shadow removal with Iterative Logistic Regression and latent Parametric Linear Combination of Gaussians

Authors: Core Francisco Park, Maya Nasr, Manuel Pérez-Carrasco, Eleanor Walker, Douglas Finkbeiner, Cecilia Garraffo

Abstract: Shadow detection and removal is a challenging problem in the analysis of hyperspectral images. Yet, this step is crucial for analyzing data for remote sensing applications like methane detection. In this work, we develop a shadow detection and removal method only based on the spectrum of each pixel and the overall distribution of spectral values. We first introduce Iterative Logistic Regression (I… ▽ More Shadow detection and removal is a challenging problem in the analysis of hyperspectral images. Yet, this step is crucial for analyzing data for remote sensing applications like methane detection. In this work, we develop a shadow detection and removal method only based on the spectrum of each pixel and the overall distribution of spectral values. We first introduce Iterative Logistic Regression (ILR) to learn a spectral basis in which shadows can be linearly classified. We then model the joint distribution of the mean radiance and the projection coefficients of the spectra onto the above basis as a parametric linear combination of Gaussians. We can then extract the maximum likelihood mixing parameter of the Gaussians to estimate the shadow coverage and to correct the shadowed spectra. Our correction scheme reduces correction artefacts at shadow borders. The shadow detection and removal method is applied to hyperspectral images from MethaneAIR, a precursor to the satellite MethaneSAT. △ Less

Submitted 23 December, 2023; originally announced December 2023.

Observational Signatures of AGN Feedback in the Morphology and the Ionization States of Milky Way-like Galaxies

Authors: Nadia Qutob, Razieh Emami, Kung-Yi Su, Randall Smith, Lars Hernquist, Dian P. Triani, Cameron Hummels, Drummond Fielding, Philip F. Hopkins, Rachel S. Somerville, David R. Ballantyne, Mark Vogelsberger, Grant Tremblay, James F. Steiner, Douglas Finkbeiner, Ramesh Narayan, Minjung Park, Josh Grindlay, Priyamvada Natarajan, Christopher C. Hayward, Dušan Kereš, Sam B. Ponnada, Sirio Belli, Rebecca Davies, Gabriel Maheson , et al. (2 additional authors not shown)

Abstract: We make an in-depth analysis of different AGN jet models' signatures, inducing quiescence in galaxies with a halo mass of $10^{12} M_\odot$. Three jet models, including cosmic ray-dominant, hot thermal, and precessing kinetic jets, are studied at two energy flux levels each, compared to a jet-free, stellar feedback-only simulation. We examine the distribution of Mg II, O VI, and O VIII ions, along… ▽ More We make an in-depth analysis of different AGN jet models' signatures, inducing quiescence in galaxies with a halo mass of $10^{12} M_\odot$. Three jet models, including cosmic ray-dominant, hot thermal, and precessing kinetic jets, are studied at two energy flux levels each, compared to a jet-free, stellar feedback-only simulation. We examine the distribution of Mg II, O VI, and O VIII ions, alongside gas temperature and density profiles. Low-energy ions, like Mg II, concentrate in the ISM, while higher energy ions, e.g., O VIII, prevail at the AGN jet cocoon's edge. High-energy flux jets display an isotropic ion distribution with lower overall density. High-energy thermal or cosmic ray jets pressurize at smaller radii, significantly suppressing core density. The cosmic ray jet provides extra pressure support, extending cool and warm gas distribution. A break in the ion-to-mass ratio slope in O VI and O VIII is demonstrated in the ISM-to-CGM transition (between 10-30 kpc), growing smoothly towards the CGM at greater distances. △ Less

Submitted 22 December, 2023; originally announced December 2023.

Comments: 17 pages, 13 figures

Cosmological Field Emulation and Parameter Inference with Diffusion Models

Authors: Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner

Abstract: Cosmological simulations play a crucial role in elucidating the effect of physical parameters on the statistics of fields and on constraining parameters given information on density fields. We leverage diffusion generative models to address two tasks of importance to cosmology -- as an emulator for cold dark matter density fields conditional on input cosmological parameters $Ω_m$ and $σ_8$, and as… ▽ More Cosmological simulations play a crucial role in elucidating the effect of physical parameters on the statistics of fields and on constraining parameters given information on density fields. We leverage diffusion generative models to address two tasks of importance to cosmology -- as an emulator for cold dark matter density fields conditional on input cosmological parameters $Ω_m$ and $σ_8$, and as a parameter inference model that can return constraints on the cosmological parameters of an input field. We show that the model is able to generate fields with power spectra that are consistent with those of the simulated target distribution, and capture the subtle effect of each parameter on modulations in the power spectrum. We additionally explore their utility as parameter inference models and find that we can obtain tight constraints on cosmological parameters. △ Less

Submitted 12 December, 2023; originally announced December 2023.

Comments: 7 pages, 5 figures, Accepted at the Machine Learning and the Physical Sciences workshop, NeurIPS 2023

Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers

Authors: D. Huppenkothen, M. Ntampaka, M. Ho, M. Fouesneau, B. Nord, J. E. G. Peek, M. Walmsley, J. F. Wu, C. Avestruz, T. Buck, M. Brescia, D. P. Finkbeiner, A. D. Goulding, T. Kacprzak, P. Melchior, M. Pasquato, N. Ramachandra, Y. -S. Ting, G. van de Ven, S. Villar, V. A. Villar, E. Zinger

Abstract: Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best pr… ▽ More Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method. △ Less

Submitted 19 October, 2023; originally announced October 2023.

Comments: 14 pages, 3 figures; submitted to the Bulletin of the American Astronomical Society

Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows

Authors: Mayeul Aubin, Carolina Cuesta-Lazaro, Ethan Tregidga, Javier Viaña, Cecilia Garraffo, Iouli E. Gordon, Mercedes López-Morales, Robert J. Hargreaves, Vladimir Yu. Makhnev, Jeremy J. Drake, Douglas P. Finkbeiner, Phillip Cargile

Abstract: Advancements in space telescopes have opened new avenues for gathering vast amounts of data on exoplanet atmosphere spectra. However, accurately extracting chemical and physical properties from these spectra poses significant challenges due to the non-linear nature of the underlying physics. This paper presents novel machine learning models developed by the AstroAI team for the Ariel Data Challe… ▽ More Advancements in space telescopes have opened new avenues for gathering vast amounts of data on exoplanet atmosphere spectra. However, accurately extracting chemical and physical properties from these spectra poses significant challenges due to the non-linear nature of the underlying physics. This paper presents novel machine learning models developed by the AstroAI team for the Ariel Data Challenge 2023, where one of the models secured the top position among 293 competitors. Leveraging Normalizing Flows, our models predict the posterior probability distribution of atmospheric parameters under different atmospheric assumptions. Moreover, we introduce an alternative model that exhibits higher performance potential than the winning model, despite scoring lower in the challenge. These findings highlight the need to reevaluate the evaluation metric and prompt further exploration of more efficient and accurate approaches for exoplanet atmosphere spectra analysis. Finally, we present recommendations to enhance the challenge and models, providing valuable insights for future applications on real observational data. These advancements pave the way for more effective and timely analysis of exoplanet atmospheric properties, advancing our understanding of these distant worlds. △ Less

Submitted 17 September, 2023; originally announced September 2023.

Comments: Conference proceeding for the ECML PKDD 2023

A parsec-scale Galactic 3D dust map out to 1.25 kpc from the Sun

Authors: Gordian Edenhofer, Catherine Zucker, Philipp Frank, Andrew K. Saydjari, Joshua S. Speagle, Douglas Finkbeiner, Torsten Enßlin

Abstract: 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. 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. We leveraged distance and extinction estim… ▽ More 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. 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. 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. 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. 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. △ Less

Submitted 26 February, 2024; v1 submitted 2 August, 2023; originally announced August 2023.

Comments: 27 pages, 24 figures, 1 table, to be published in Astronomy & Astrophysics (A&A)

Roman Early-Definition Astrophysics Survey Opportunity: Galactic Roman Infrared Plane Survey (GRIPS)

Authors: Roberta Paladini, Catherine Zucker, Robert Benjamin, David Nataf, Dante Minniti, Gail Zasowski, Joshua Peek, Sean Carey, Lori Allen, Javier Alonso-Garcia, Joao Alves, Friederich Anders, Evangelie Athanassoula, Timothy C. Beers, Jonathan Bird, Joss Bland-Hwathorn, Anthony Brown, Sven Buder, Luca Casagrande, Andrew Casey, Santi Cassisi, Marcio Catelan, Ranga-Ram Chary, Andre-Nicolas Chene, David Ciardi , et al. (45 additional authors not shown)

Abstract: 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 yiel… ▽ More 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. △ Less

Submitted 14 July, 2023; originally announced July 2023.

Comments: Submitted to the Roman Project on October 22 2021 in response to a call for white papers on early-definition Astrophysics opportunity

NANCY: Next-generation All-sky Near-infrared Community surveY

Authors: Jiwon Jesse Han, Arjun Dey, Adrian M. Price-Whelan, Joan Najita, Edward F. Schlafly, Andrew Saydjari, Risa H. Wechsler, Ana Bonaca, David J Schlegel, Charlie Conroy, Anand Raichoor, Alex Drlica-Wagner, Juna A. Kollmeier, Sergey E. Koposov, Gurtina Besla, Hans-Walter Rix, Alyssa Goodman, Douglas Finkbeiner, Abhijeet Anand, Matthew Ashby, Benedict Bahr-Kalus, Rachel Beaton, Jayashree Behera, Eric F. Bell, Eric C Bellm , et al. (184 additional authors not shown)

Abstract: 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, GAL… ▽ More 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. △ Less

Submitted 20 June, 2023; originally announced June 2023.

Comments: Submitted to the call for white papers for the Roman Core Community Survey (June 16th, 2023), and to the Bulletin of the AAS

The EB-correlation in Resolved Polarized Images: Connections to Astrophysics of Black Holes

Authors: Razieh Emami, Sheperd S. Doeleman, Maciek Wielgus, Dominic Chang, Koushik Chatterjee, Randall Smith, Matthew Liska, James F. Steiner, Angelo Ricarte, Ramesh Narayan, Grant Tremblay, Douglas Finkbeiner, Lars Hernquist, Chi-Kwan Chan, Lindy Blackburn, Ben S. Prather, Paul Tiede, Avery E. Broderick, Mark Vogelsberger, Charles Alcock, Freek Roelofs

Abstract: We present an in-depth analysis of a newly proposed correlation function in visibility space, between the E and B modes of the linear polarization, hereafter the EB-correlation, for a set of time-averaged GRMHD simulations compared with the phase map from different semi-analytic models as well as the Event Horizon Telescope (EHT) 2017 data for M87* source. We demonstrate that the phase map of the… ▽ More We present an in-depth analysis of a newly proposed correlation function in visibility space, between the E and B modes of the linear polarization, hereafter the EB-correlation, for a set of time-averaged GRMHD simulations compared with the phase map from different semi-analytic models as well as the Event Horizon Telescope (EHT) 2017 data for M87* source. We demonstrate that the phase map of the time-averaged EB-correlation contains novel information that might be linked to the BH spin, accretion state and the electron temperature. A detailed comparison with a semi-analytic approach with different azimuthal expansion modes shows that to recover the morphology of the real/imaginary part of the correlation function and its phase, we require higher orders of these azimuthal modes. To extract the phase features, we propose to use the Zernike polynomial reconstruction developing an empirical metric to break degeneracies between models with different BH spins that are qualitatively similar. We use a set of different geometrical ring models with various magnetic and velocity field morphologies and show that both the image space and visibility based EB-correlation morphologies in MAD simulations can be explained with simple fluid and magnetic field geometries as used in ring models. SANEs by contrast are harder to model, demonstrating that the simple fluid and magnetic field geometries of ring models are not sufficient to describe them owing to higher Faraday Rotation depths. A qualitative comparison with the EHT data demonstrates that some of the features in the phase of EB-correlation might be well explained by the current models for BH spins as well as electron temperatures, while others may require a larger theoretical surveys. △ Less

Submitted 3 May, 2023; v1 submitted 30 April, 2023; originally announced May 2023.

Comments: 26 pages, 21 Figures

Stellar Reddening Based Extinction Maps for Cosmological Applications

Authors: Nayantara Mudur, Core Francisco Park, Douglas P Finkbeiner

Abstract: Cosmological surveys must correct their observations for the reddening of extragalactic objects by Galactic dust. Existing dust maps, however, have been found to have spatial correlations with the large-scale structure of the Universe. Errors in extinction maps can propagate systematic biases into samples of dereddened extragalactic objects and into cosmological measurements such as correlation fu… ▽ More Cosmological surveys must correct their observations for the reddening of extragalactic objects by Galactic dust. Existing dust maps, however, have been found to have spatial correlations with the large-scale structure of the Universe. Errors in extinction maps can propagate systematic biases into samples of dereddened extragalactic objects and into cosmological measurements such as correlation functions between foreground lenses and background objects and the primordial non-gaussianity parameter $f_{NL}$. Emission-based maps are contaminated by the cosmic infrared background, while maps inferred from stellar-reddenings suffer from imperfect removal of quasars and galaxies from stellar catalogs. Thus, stellar-reddening based maps using catalogs without extragalactic objects offer a promising path to making dust maps with minimal correlations with large-scale structure. We present two high-latitude integrated extinction maps based on stellar reddenings, with a point spread function of full-width half-maximum 6.1' and 15'. We employ a strict selection of catalog objects to filter out galaxies and quasars and measure the spatial correlation of our extinction maps with extragalactic structure. Our galactic extinction maps have reduced spatial correlation with large scale structure relative to most existing stellar-reddening based and emission-based extinction maps. △ Less

Submitted 8 December, 2022; originally announced December 2022.

Comments: 21 pages, 10 figures

Measuring the 8621 Å Diffuse Interstellar Band in Gaia DR3 RVS Spectra: Obtaining a Clean Catalog by Marginalizing over Stellar Types

Authors: Andrew K. Saydjari, Ana Sofía M. Uzsoy, Catherine Zucker, J. E. G. Peek, Douglas P. Finkbeiner

Abstract: Diffuse interstellar bands (DIBs) are broad absorption features associated with interstellar dust and can serve as chemical and kinematic tracers. Conventional measurements of DIBs in stellar spectra are complicated by residuals between observations and best-fit stellar models. To overcome this, we simultaneously model the spectrum as a combination of stellar, dust, and residual components, with f… ▽ More Diffuse interstellar bands (DIBs) are broad absorption features associated with interstellar dust and can serve as chemical and kinematic tracers. Conventional measurements of DIBs in stellar spectra are complicated by residuals between observations and best-fit stellar models. To overcome this, we simultaneously model the spectrum as a combination of stellar, dust, and residual components, with full posteriors on the joint distribution of the components. This decomposition is obtained by modeling each component as a draw from a high-dimensional Gaussian distribution in the data-space (the observed spectrum) -- a method we call "Marginalized Analytic Data-space Gaussian Inference for Component Separation" (MADGICS). We use a data-driven prior for the stellar component, which avoids missing stellar features not well-modeled by synthetic spectra. This technique provides statistically rigorous uncertainties and detection thresholds, which are required to work in the low signal-to-noise regime that is commonplace for dusty lines of sight. We reprocess all public Gaia DR3 RVS spectra and present an improved 8621 Å DIB catalog, free of detectable stellar line contamination. We constrain the rest-frame wavelength to $8623.14 \pm 0.087$ Å (vacuum), find no significant evidence for DIBs in the Local Bubble from the $1/6^{\rm{th}}$ of RVS spectra that are public, and show unprecedented correlation with kinematic substructure in Galactic CO maps. We validate the catalog, its reported uncertainties, and biases using synthetic injection tests. We believe MADGICS provides a viable path forward for large-scale spectral line measurements in the presence of complex spectral contamination. △ Less

Submitted 30 August, 2023; v1 submitted 7 December, 2022; originally announced December 2022.

Comments: 28 pages, 27 figures, accepted at ApJ

Can denoising diffusion probabilistic models generate realistic astrophysical fields?

Authors: Nayantara Mudur, Douglas P. Finkbeiner

Abstract: Score-based generative models have emerged as alternatives to generative adversarial networks (GANs) and normalizing flows for tasks involving learning and sampling from complex image distributions. In this work we investigate the ability of these models to generate fields in two astrophysical contexts: dark matter mass density fields from cosmological simulations and images of interstellar dust.… ▽ More Score-based generative models have emerged as alternatives to generative adversarial networks (GANs) and normalizing flows for tasks involving learning and sampling from complex image distributions. In this work we investigate the ability of these models to generate fields in two astrophysical contexts: dark matter mass density fields from cosmological simulations and images of interstellar dust. We examine the fidelity of the sampled cosmological fields relative to the true fields using three different metrics, and identify potential issues to address. We demonstrate a proof-of-concept application of the model trained on dust in denoising dust images. To our knowledge, this is the first application of this class of models to the interstellar medium. △ Less

Submitted 22 November, 2022; originally announced November 2022.

Comments: 8 pages, 3 figures, Accepted at the Machine Learning and the Physical Sciences workshop, NeurIPS 2022

The First 3$π$ 3D Map of ISM Dust Temperature

Authors: Ioana A. Zelko, Douglas P. Finkbeiner, Albert Lee, Gregory Green

Abstract: We present the first large-scale 3D map of interstellar dust temperature. We build upon existing 3D reddening maps derived from starlight absorption (Bayestar19), covering 3/4 of the sky. Starting with the column density for each of 500 million 3D voxels, we propose a temperature and emissivity power-law slope ($β$) for each of them, and integrate along the line of sight to synthesize an emission… ▽ More We present the first large-scale 3D map of interstellar dust temperature. We build upon existing 3D reddening maps derived from starlight absorption (Bayestar19), covering 3/4 of the sky. Starting with the column density for each of 500 million 3D voxels, we propose a temperature and emissivity power-law slope ($β$) for each of them, and integrate along the line of sight to synthesize an emission map in five frequency bands observed by \emph{Planck} and \emph{IRAS}. The reconstructed emission map is constrained to match observations on a $10'$ scale, and does so with good fidelity. We produce 3D temperature maps at resolutions of $110', 55', $and $27'$. We assess performance on Cepheus, a dust cloud with two distinct components along the line of sight, and find distinct temperatures for the two components. We thus show that this methodology has enough precision to constrain clouds with different temperature along the line of sight up to $1-σ$ error. This would be an important result for dust frequency decorrelation foreground analysis for cosmic microwave background experiments, which would be impacted by a line-of-sight with varying temperature and magnetic field components. In addition to $T$ and $β$, we constrain the conversion factor between emission optical depth and reddening. This conversion factor is assumed to be constant in commonly used emission-based reddening maps. However, this work shows a factor of two variation that may prove significant for some applications. △ Less

Submitted 14 November, 2022; originally announced November 2022.

Comments: 11 Figures, 19 pages

SARABANDE: 3/4 Point Correlation Functions with Fast Fourier Transforms

Authors: James Sunseri, Zachary Slepian, Stephen Portillo, Jiamin Hou, Sule Kahraman, Douglas P. Finkbeiner

Abstract: We present a new $\texttt{python}$ package SARABANDE for measuring 3 & 4 Point Correlation Functions (3/4 PCFs) in $\mathcal{O}(N_{\rm g} \log N_{\rm g})$ time using Fast Fourier Transforms (FFTs), with $N_{\rm g}$ the number of grid points used for the FFT. SARABANDE can measure both projected and full 3 and 4 PCFs on gridded 2D and 3D datasets. The general technique is to generate suitable angul… ▽ More We present a new $\texttt{python}$ package SARABANDE for measuring 3 & 4 Point Correlation Functions (3/4 PCFs) in $\mathcal{O}(N_{\rm g} \log N_{\rm g})$ time using Fast Fourier Transforms (FFTs), with $N_{\rm g}$ the number of grid points used for the FFT. SARABANDE can measure both projected and full 3 and 4 PCFs on gridded 2D and 3D datasets. The general technique is to generate suitable angular basis functions on an underlying grid, radially bin these to create kernels, and convolve these kernels with the original gridded data to obtain expansion coefficients about every point simultaneously. These coefficients are then combined to give us the 3/4 PCF as expanded in our basis. We apply SARABANDE to simulations of the Interstellar Medium (ISM) to show the results and scaling of calculating both the full and projected 3/4 PCFs. △ Less

Submitted 25 October, 2022; v1 submitted 18 October, 2022; originally announced October 2022.

Comments: 16 Pages, 8 Figures, 8 Algorithms, 1 code package

The MegaMapper: A Stage-5 Spectroscopic Instrument Concept for the Study of Inflation and Dark Energy

Authors: David J. Schlegel, Juna A. Kollmeier, Greg Aldering, Stephen Bailey, Charles Baltay, Christopher Bebek, Segev BenZvi, Robert Besuner, Guillermo Blanc, Adam S. Bolton, Ana Bonaca, Mohamed Bouri, David Brooks, Elizabeth Buckley-Geer, Zheng Cai, Jeffrey Crane, Regina Demina, Joseph DeRose, Arjun Dey, Peter Doel, Xiaohui Fan, Simone Ferraro, Douglas Finkbeiner, Andreu Font-Ribera, Satya Gontcho A Gontcho , et al. (64 additional authors not shown)

Abstract: In this white paper, we present the MegaMapper concept. The MegaMapper is a proposed ground-based experiment to measure Inflation parameters and Dark Energy from galaxy redshifts at $2<z<5$. In order to achieve path-breaking results with a mid-scale investment, the MegaMapper combines existing technologies for critical path elements and pushes innovative development in other design areas. To this… ▽ More In this white paper, we present the MegaMapper concept. The MegaMapper is a proposed ground-based experiment to measure Inflation parameters and Dark Energy from galaxy redshifts at $2<z<5$. In order to achieve path-breaking results with a mid-scale investment, the MegaMapper combines existing technologies for critical path elements and pushes innovative development in other design areas. To this aim, we envision a 6.5-m Magellan-like telescope, with a newly designed wide field, coupled with DESI spectrographs, and small-pitch robots to achieve multiplexing of at least 26,000. This will match the expected achievable target density in the redshift range of interest and provide a 10x capability over the existing state-of the art, without a 10x increase in project budget. △ Less

Submitted 9 September, 2022; originally announced September 2022.

Comments: Contributed White Paper to Snowmass 2021. arXiv admin note: substantial text overlap with arXiv:1907.11171. text overlap with arXiv:2209.03585

The Dark Energy Camera Plane Survey 2 (DECaPS2): More Sky, Less Bias, and Better Uncertainties

Authors: A. K. Saydjari, E. F. Schlafly, D. Lang, A. M. Meisner, G. M. Green, C. Zucker, I. Zelko, J. S. Speagle, T. Daylan, A. Lee, F. Valdes, D. Schlegel, D. P. Finkbeiner

Abstract: 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 DECam Plane Survey (DECaPS2) 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-expo… ▽ More 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 DECam Plane Survey (DECaPS2) 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 arcsecond seeing. The survey comprises 3.32 billion objects built from 34 billion detections in 21.4 thousand exposures, totaling 260 hours open shutter time on the Dark Energy Camera (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 post-processing technique was applied to every detection to de-bias 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/. △ Less

Submitted 26 July, 2022; v1 submitted 23 June, 2022; originally announced June 2022.

Comments: 40 pages, 33 figures, submitted to ApJS (v2 reduces figure file sizes and fixes small bug in z-band source density plot)

Quantification of high dimensional non-Gaussianities and its implication to Fisher analysis in cosmology

Authors: Core Francisco Park, Erwan Allys, Francisco Villaescusa-Navarro, Douglas P. Finkbeiner

Abstract: It is well known that the power spectrum is not able to fully characterize the statistical properties of non-Gaussian density fields. Recently, many different statistics have been proposed to extract information from non-Gaussian cosmological fields that perform better than the power spectrum. The Fisher matrix formalism is commonly used to quantify the accuracy with which a given statistic can co… ▽ More It is well known that the power spectrum is not able to fully characterize the statistical properties of non-Gaussian density fields. Recently, many different statistics have been proposed to extract information from non-Gaussian cosmological fields that perform better than the power spectrum. The Fisher matrix formalism is commonly used to quantify the accuracy with which a given statistic can constrain the value of the cosmological parameters. However, these calculations typically rely on the assumption that the likelihood of the considered statistic follows a multivariate Gaussian distribution. In this work we follow Sellentin & Heavens (2017) and use two different statistical tests to identify non-Gaussianities in different statistics such as the power spectrum, bispectrum, marked power spectrum, and wavelet scatering transform (WST). We remove the non-Gaussian components of the different statistics and perform Fisher matrix calculations with the \textit{Gaussianized} statistics using Quijote simulations. We show that constraints on the parameters can change by a factor of $\sim 2$ in some cases. We show with simple examples how statistics that do not follow a multivariate Gaussian distribution can achieve artificially tight bounds on the cosmological parameters when using the Fisher matrix formalism. We think that the non-Gaussian tests used in this work represent a powerful tool to quantify the robustness of Fisher matrix calculations and their underlying assumptions. We release the code used to compute the power spectra, bispectra, and WST that can be run on both CPUs and GPUs. △ Less

Submitted 11 April, 2022; originally announced April 2022.

Comments: 24 pages, 6 figures

Photometry on Structured Backgrounds: Local Pixelwise Infilling by Regression

Authors: Andrew K. Saydjari, Douglas P. Finkbeiner

Abstract: Photometric pipelines struggle to estimate both the flux and flux uncertainty for stars in the presence of structured backgrounds such as filaments or clouds. However, it is exactly stars in these complex regions that are critical to understanding star formation and the structure of the interstellar medium. We develop a method, similar to Gaussian process regression, which we term local pixelwise… ▽ More Photometric pipelines struggle to estimate both the flux and flux uncertainty for stars in the presence of structured backgrounds such as filaments or clouds. However, it is exactly stars in these complex regions that are critical to understanding star formation and the structure of the interstellar medium. We develop a method, similar to Gaussian process regression, which we term local pixelwise infilling (LPI). Using a local covariance estimate, we predict the background behind each star and the uncertainty on that prediction in order to improve estimates of flux and flux uncertainty. We show the validity of our model on synthetic data and real dust fields. We further demonstrate that the method is stable even in the crowded field limit. While we focus on optical-IR photometry, this method is not restricted to those wavelengths. We apply this technique to the 34 billion detections in the second data release of the Dark Energy Camera Plane Survey (DECaPS2). In addition to removing many $>3σ$ outliers and improving uncertainty estimates by a factor of $\sim 2-3$ on nebulous fields, we also show that our method is well-behaved on uncrowded fields. The entirely post-processing nature of our implementation of LPI photometry allows it to easily improve the flux and flux uncertainty estimates of past as well as future surveys. △ Less

Submitted 18 January, 2022; originally announced January 2022.

Comments: 22 pages, 15 pages, submitted to ApJ

Star formation near the Sun is driven by expansion of the Local Bubble

Authors: Catherine Zucker, Alyssa A. Goodman, João Alves, Shmuel Bialy, Michael Foley, Joshua S. Speagle, Josefa Großschedl, Douglas P. Finkbeiner, Andreas Burkert, Diana Khimey, Cameren Swiggum

Abstract: 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 interstell… ▽ More 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. Leveraging new spatial and dynamical constraints from the Gaia space mission, here we report an analysis of the 3D positions, shapes, and motions of dense gas and young stars within 200 pc of the Sun. We find that nearly all 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 scenario where the origin of the Local Bubble was a burst of stellar birth and then death (supernovae) taking place near the bubble's center beginning 14 Myr ago. The expansion of the Local Bubble created by the supernovae swept up the ambient interstellar medium into an extended shell that has now fragmented and collapsed into the most prominent nearby molecular clouds, in turn providing robust observational support for the theory of supernova-driven star formation. △ Less

Submitted 13 January, 2022; originally announced January 2022.

Comments: Published in Nature on 12 January 2022. For data, interactive visualizations, and more information see https://sites.google.com/cfa.harvard.edu/local-bubble-star-formation

Journal ref: Nature 601, 334-337 (2022)

On the Three-Dimensional Structure of Local Molecular Clouds

Authors: Catherine Zucker, Alyssa Goodman, João Alves, Shmuel Bialy, Eric W. Koch, Joshua S. Speagle, Michael M. Foley, Douglas Finkbeiner, Reimar Leike, Torsten Enßlin, Joshua E. G. Peek, Gordian Edenhofer

Abstract: We leverage the 1 pc spatial resolution of the Leike et al. 2020 3D dust map to characterize the three-dimensional structure of nearby molecular clouds ($d \lesssim 400$ 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 $\approx 1\%$ uncertainty. For each cloud, we determine an average r… ▽ More We leverage the 1 pc spatial resolution of the Leike et al. 2020 3D dust map to characterize the three-dimensional structure of nearby molecular clouds ($d \lesssim 400$ 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 $\approx 1\%$ 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 $\approx 3:1$. 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 2D 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 ($A_V \gtrsim 2 - 3$ 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. △ Less

Submitted 27 September, 2021; v1 submitted 20 September, 2021; originally announced September 2021.

Comments: Published in ApJ. For data behind the figures see https://dataverse.harvard.edu/dataverse/3D_cloud_structure . For interactive gallery of local clouds see https://faun.rc.fas.harvard.edu/czucker/Paper_Figures/3D_Cloud_Topologies/gallery.html . Companion paper to Bialy et al. 2021 (arXiv:2109.09763). For press see https://www.cfa.harvard.edu/news/gigantic-cavity-space-sheds-new-light-how-stars-form

Journal ref: ApJ 919 35 (2021)

Improved Latent Tree Induction with Distant Supervision via Span Constraints

Authors: Zhiyang Xu, Andrew Drozdov, Jay Yoon Lee, Tim O'Gorman, Subendhu Rongali, Dylan Finkbeiner, Shilpa Suresh, Mohit Iyyer, Andrew McCallum

Abstract: For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems still do not perform well enough compared to their supervised counterparts to have any practical use as structural annotation of text. In this work, we present a technique that uses distant supervision in the form of span co… ▽ More For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems still do not perform well enough compared to their supervised counterparts to have any practical use as structural annotation of text. In this work, we present a technique that uses distant supervision in the form of span constraints (i.e. phrase bracketing) to improve performance in unsupervised constituency parsing. Using a relatively small number of span constraints we can substantially improve the output from DIORA, an already competitive unsupervised parsing system. Compared with full parse tree annotation, span constraints can be acquired with minimal effort, such as with a lexicon derived from Wikipedia, to find exact text matches. Our experiments show span constraints based on entities improves constituency parsing on English WSJ Penn Treebank by more than 5 F1. Furthermore, our method extends to any domain where span constraints are easily attainable, and as a case study we demonstrate its effectiveness by parsing biomedical text from the CRAFT dataset. △ Less

Submitted 1 November, 2021; v1 submitted 10 September, 2021; originally announced September 2021.

Comments: EMNLP 2021

Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering Transforms

Authors: Andrew K. Saydjari, Douglas P. Finkbeiner

Abstract: Wavelet scattering networks, which are convolutional neural networks (CNNs) with fixed filters and weights, are promising tools for image analysis. Imposing symmetry on image statistics can improve human interpretability, aid in generalization, and provide dimension reduction. In this work, we introduce a fast-to-compute, translationally invariant and rotationally equivariant wavelet scattering ne… ▽ More Wavelet scattering networks, which are convolutional neural networks (CNNs) with fixed filters and weights, are promising tools for image analysis. Imposing symmetry on image statistics can improve human interpretability, aid in generalization, and provide dimension reduction. In this work, we introduce a fast-to-compute, translationally invariant and rotationally equivariant wavelet scattering network (EqWS) and filter bank of wavelets (triglets). We demonstrate the interpretability and quantify the invariance/equivariance of the coefficients, briefly commenting on difficulties with implementing scale equivariance. On MNIST, we show that training on a rotationally invariant reduction of the coefficients maintains rotational invariance when generalized to test data and visualize residual symmetry breaking terms. Rotation equivariance is leveraged to estimate the rotation angle of digits and reconstruct the full rotation dependence of each coefficient from a single angle. We benchmark EqWS with linear classifiers on EMNIST and CIFAR-10/100, introducing a new second-order, cross-color channel coupling for the color images. We conclude by comparing the performance of an isotropic reduction of the scattering coefficients and RWST, a previous coefficient reduction, on an isotropic classification of magnetohydrodynamic simulations with astrophysical relevance. △ Less

Submitted 22 April, 2021; originally announced April 2021.

Comments: 20 pages, 14 figures, submitted to IEEE Signal Processing

Classification of Magnetohydrodynamic Simulations using Wavelet Scattering Transforms

Authors: Andrew K. Saydjari, Stephen K. N. Portillo, Zachary Slepian, Sule Kahraman, Blakesley Burkhart, Douglas P. Finkbeiner

Abstract: The complex interplay of magnetohydrodynamics, gravity, and supersonic turbulence in the interstellar medium (ISM) introduces non-Gaussian structure that can complicate comparison between theory and observation. We show that the Wavelet Scattering Transform (WST), in combination with linear discriminant analysis (LDA), is sensitive to non-Gaussian structure in 2D ISM dust maps. WST-LDA classifies… ▽ More The complex interplay of magnetohydrodynamics, gravity, and supersonic turbulence in the interstellar medium (ISM) introduces non-Gaussian structure that can complicate comparison between theory and observation. We show that the Wavelet Scattering Transform (WST), in combination with linear discriminant analysis (LDA), is sensitive to non-Gaussian structure in 2D ISM dust maps. WST-LDA classifies magnetohydrodynamic (MHD) turbulence simulations with up to a 97\% true positive rate in our testbed of 8 simulations with varying sonic and Alfvénic Mach numbers. We present a side-by-side comparison with two other methods for non-Gaussian characterization, the Reduced Wavelet Scattering Transform (RWST) and the 3-Point Correlation Function (3PCF). We also demonstrate the 3D-WST-LDA and apply it to classification of density fields in position-position-velocity (PPV) space, where density correlations can be studied using velocity coherence as a proxy. WST-LDA is robust to common observational artifacts, such as striping and missing data, while also sensitive enough to extract the net magnetic field direction for sub-Alfvénic turbulent density fields. We include a brief analysis of the effect of point spread functions and image pixelization on 2D-WST-LDA applied to density fields, which informs the future goal of applying WST-LDA to 2D or 3D all-sky dust maps to extract hydrodynamic parameters of interest. △ Less

Submitted 22 October, 2020; originally announced October 2020.

Comments: 21 pages, 13 figures, submitted to ApJ

The Catalogue for Astrophysical Turbulence Simulations (CATS)

Authors: B. Burkhart, S. Appel, S. Bialy, J. Cho, A. J. Christensen, D. Collins, C. Federrath, D. Fielding, D. Finkbeiner, A. S. Hill, J. C. Ibanez-Mejia, M. R. Krumholz, A. Lazarian, M. Li, P. Mocz, M. -M. Mac Low, J. Naiman, S. K. N. Portillo, B. Shane, Z. Slepian, Y. Yuan

Abstract: Turbulence is a key process in many fields of astrophysics. Advances in numerical simulations of fluids over the last several decades have revolutionized our understanding of turbulence and related processes such as star formation and cosmic ray propagation. However, data from numerical simulations of astrophysical turbulence are often not made public. We introduce a new simulation-oriented databa… ▽ More Turbulence is a key process in many fields of astrophysics. Advances in numerical simulations of fluids over the last several decades have revolutionized our understanding of turbulence and related processes such as star formation and cosmic ray propagation. However, data from numerical simulations of astrophysical turbulence are often not made public. We introduce a new simulation-oriented database for the astronomical community: The Catalogue for Astrophysical Turbulence Simulations (CATS), located at www.mhdturbulence.com. CATS includes magnetohydrodynamic (MHD) turbulent box simulation data products generated by the public codes athena++, arepo, enzo, and flash. CATS also includes several synthetic observational data sets, such as turbulent HI data cubes. We also include measured power spectra and 3-point correlation functions from some of these data. We discuss the importance of open source statistical and visualization tools for the analysis of turbulence simulations such as those found in CATS. △ Less

Submitted 21 October, 2020; originally announced October 2020.

Comments: Accepted to ApJ

Impact of Dust on Spectral Distortion Measurements of the Cosmic Microwave Background

Authors: Ioana A. Zelko, Douglas P. Finkbeiner

Abstract: Spectral distortions of the cosmic microwave background (CMB) are sensitive to energy injection by exotic physics in the early universe. The proposed Primordial Inflation Explorer (PIXIE) mission has the raw sensitivity to provide meaningful limits on new physics, but only if foreground emission can be adequately modeled. We quantify the impact of interstellar dust on Compton $y$ and $μ$ measureme… ▽ More Spectral distortions of the cosmic microwave background (CMB) are sensitive to energy injection by exotic physics in the early universe. The proposed Primordial Inflation Explorer (PIXIE) mission has the raw sensitivity to provide meaningful limits on new physics, but only if foreground emission can be adequately modeled. We quantify the impact of interstellar dust on Compton $y$ and $μ$ measurements by considering a range of grain size distributions and compositions constrained by theoretical and observational priors (Zelko & Finkbeiner 2020). We find that PIXIE can marginalize over a modest number of dust parameters and still recover $y$ and $μ$ estimates, though with increased uncertainty. As more foreground components are included (synchrotron, free-free), the estimates of $y$ degrade, and measurement of $μ$ in the range sometimes considered for the standard $Λ$CDM of $2\times10^{-8}$ becomes infeasible without ancillary low-frequency foreground information. An additional concern is dust absorption of the CMB monopole, a subtle effect that must be included. We quantify one form of model discrepancy error, finding that the error introduced by fitting our interstellar medium dust model with a modified blackbody is too large for CMB spectral distortions to be detectable. The greatest challenge may be the cosmic infrared background (CIB). We find that $μ$ and $y$ are extremely sensitive to modeling choices for the CIB, and quantify biases expected for a range of assumptions. △ Less

Submitted 21 September, 2021; v1 submitted 13 October, 2020; originally announced October 2020.

Comments: Published in the Astrophysical Journal. 25 pages, 12 figures, 10 tables. Updated to reflect the journal version. Corrected a typo in the Appendix Eq A6 and A9; updated the tables 5,6,7 (first column). There were no changes to the qualitative nature of the results

Journal ref: The Astrophysical Journal, Volume 914, Issue 1, id.68, 23 pp, June 2021

Implications of Grain Size Distribution and Composition for the Correlation Between Dust Extinction and Emissivity

Authors: Ioana A. Zelko, Douglas P. Finkbeiner

Abstract: We study the effect of variations in dust size distribution and composition on the correlation between the spectral shape of extinction (parameterized by $R_{\textrm{V}}$) and far-infrared dust emissivity (parameterized by the power-law index $β$). Starting from the size distribution models proposed by Weingartner & Draine (2001), using the dust absorption and emission properties derived by Laor &… ▽ More We study the effect of variations in dust size distribution and composition on the correlation between the spectral shape of extinction (parameterized by $R_{\textrm{V}}$) and far-infrared dust emissivity (parameterized by the power-law index $β$). Starting from the size distribution models proposed by Weingartner & Draine (2001), using the dust absorption and emission properties derived by Laor & Draine (1993) for carbonaceous and silicate grains, and by Li & Draine (2001) for PAH grains, we calculate the extinction and compare it with the reddening vector derived by Schlafly et al. (2016). An optimizer and an MCMC are used to explore the space of available parameters for the size distributions. We find that larger grains are correlated with high $R_{\textrm{V}}$. However, this trend is not enough to explain the emission-extinction correlation observed by Schlafly et al. (2016). For the $R_{\textrm{V}}-β$ correlation to arise, we need to impose explicit priors for the carbonaceous and silicate volume priors as functions of $R_{\textrm{V}}$. The results show that a composition with higher ratio of carbonaceous to silicate grains leads to higher $R_{\textrm{V}}$ and lower $β$. A relation between $E(\textrm{B}-\textrm{V})/τ_{353}$ and $R_{\textrm{V}}$ is apparent, with possible consequences for the recalibration of emission-based dust maps as a function of $R_{\textrm{V}}$. △ Less

Submitted 18 May, 2021; v1 submitted 24 September, 2020; originally announced September 2020.

Comments: Published in The Astrophysical Journal. 28 pages, 27 figures, 3 Tables; updated to correct some typos identified in the proof reading stage of the journal

Journal ref: The Astrophysical Journal, Volume 904, Issue 1, id.38, 22 pp., Year 2020

A Prospective ISRO-CfA Himalayan Sub-millimeter-wave Observatory Initiative

Authors: T. K. Sridharan, Shmuel Bialy, Raymond Blundell, Andrew Burkhardt, Thomas Dame, Sheperd Doeleman, Douglas Finkbeiner, Alyssa Goodman, Paul Grimes, Nia Imara, Michael Johnson, Garrett Keating, Charles Lada, Romane Le Gal, Philip Myers, Ramesh Narayan, Scott Paine, Nimesh Patel, Alexander Raymond, Edward Tong, David Wilner, Qizhou Zhang, Catherine Zucker

Abstract: The Smithsonian Astrophysical Observatory (SAO), a member of the Center for Astrophysics | Harvard and Smithsonian, is in discussions with the Space Applications Centre (SAC) of the Indian Space Research Organization (ISRO) and its partners in the newly formed Indian Sub-millimetre-wave Astronomy Alliance (ISAA), to collaborate in the construction of a sub-millimeter-wave astronomy observatory in… ▽ More The Smithsonian Astrophysical Observatory (SAO), a member of the Center for Astrophysics | Harvard and Smithsonian, is in discussions with the Space Applications Centre (SAC) of the Indian Space Research Organization (ISRO) and its partners in the newly formed Indian Sub-millimetre-wave Astronomy Alliance (ISAA), to collaborate in the construction of a sub-millimeter-wave astronomy observatory in the high altitude deserts of the Himalayas, initially at the 4500 m Indian Astronomical Observatory, Hanle. Two primary science goals are targeted. One is the mapping of the distribution of neutral atomic carbon, and the carbon monoxide (CO) molecule in higher energy states, in large parts of the Milky Way, and in selected external galaxies. Such studies would advance our understanding of molecular hydrogen present in the interstellar medium, but partly missed by existing observations; and characterize Galaxy-wide molecular cloud excitation conditions, through multi-level CO observations. Stars form in interstellar clouds of molecular gas and dust, and these observations would allow research into the formation and destruction processes of such molecular clouds and the life cycle of galaxies. As the second goal, the observatory would add a new location to the global Event Horizon Telescope (EHT) network, which lacks a station in the Himalayan longitudes. This addition would enhance the quality of the images synthesized by the EHT, support observations in higher sub-millimeter wave bands, sharpening its resolving ability, improve its dynamic imaging capability and add weather resilience to observing campaigns. In the broader context, this collaboration can be a starting point for a wider, mutually beneficial scientific exchange between the Indian and US astronomy communities, including a potential future EHT space component. △ Less

Submitted 7 November, 2020; v1 submitted 17 August, 2020; originally announced August 2020.

Comments: Internal white paper prepared in support of the initiative, 32 pages, 7 figures

Data-Driven Stellar Models

Authors: Gregory M. Green, Hans-Walter Rix, Leon Tschesche, Douglas Finkbeiner, Catherine Zucker, Edward F. Schlafly, Jan Rybizki, Morgan Fouesneau, René Andrae, Joshua Speagle

Abstract: 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… ▽ More 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. △ Less

Submitted 29 March, 2021; v1 submitted 29 June, 2020; originally announced June 2020.

Comments: 30 pages, 13 figures. Revised version published in ApJ, including new Fig. 13

Journal ref: The Astrophysical Journal, Volume 907, Issue 1, id.57, 17 pp., January 2021

Observing Strategy for the Legacy Surveys

Authors: Kaylan J. Burleigh, Martin Landriau, Arjun Dey, Dustin Lang, David J. Schlegel, Peter E. Nugent, Robert Blum, Joseph R. Findlay, Douglas P. Finkbeiner, David Herrera, Klaus Honscheid, Stéphanie Juneau, Ian McGreer, Aaron M. Meisner, John Moustakas, Adam D. Myers, Anna Patej, Edward F. Schlafly, Francisco Valdes, Alistair R. Walker, Benjamin A. Weaver, Christophe Yèche

Abstract: The Legacy Surveys, a combination of three ground-based imaging surveys, have mapped 16,000 deg$^2$ in three optical bands ($g$, $r$, and $z$) to a depth 1--$2$~mag deeper than the Sloan Digital Sky Survey (SDSS). Our work addresses one of the major challenges of wide-field imaging surveys conducted at ground-based observatories: the varying depth that results from varying observing conditions at… ▽ More The Legacy Surveys, a combination of three ground-based imaging surveys, have mapped 16,000 deg$^2$ in three optical bands ($g$, $r$, and $z$) to a depth 1--$2$~mag deeper than the Sloan Digital Sky Survey (SDSS). Our work addresses one of the major challenges of wide-field imaging surveys conducted at ground-based observatories: the varying depth that results from varying observing conditions at Earth-bound sites. To mitigate these effects, two of the Legacy Surveys (the Dark Energy Camera Legacy Survey, or DECaLS; and the Mayall $z$-band Legacy Survey, or MzLS) employed a unique strategy to dynamically adjust the exposure times as rapidly as possible in response to the changing observing conditions. We present the tiling and observing strategies used by these surveys. We demonstrate that the tiling and dynamic observing strategies jointly result in a more uniform-depth survey that has higher efficiency for a given total observing time compared with the traditional approach of using fixed exposure times. △ Less

Submitted 9 July, 2020; v1 submitted 13 February, 2020; originally announced February 2020.

Comments: v1: 14 pages, 3 tables and 5 figures; v2: 15 pages, 3 tables and 6 figures. Changes in response to referee comments; matches published version

Journal ref: AJ, 160:61 (2020)

A Galactic-scale gas wave in the Solar Neighborhood

Authors: João Alves, Catherine Zucker, Alyssa A. Goodman, Joshua S. Speagle, Stefan Meingast, Thomas Robitaille, Douglas P. Finkbeiner, Edward F. Schlafly, Gregory M. Green

Abstract: 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 thei… ▽ More 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. △ Less

Submitted 23 January, 2020; originally announced January 2020.

Comments: Published in Nature on 7 January 2020. For data, interactive visualizations, and more information see: http://tinyurl.com/radwave (https://sites.google.com/cfa.harvard.edu/radcliffewave/)

Journal ref: Nature, 7 January 2020

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

Authors: Catherine Zucker, Joshua S. Speagle, Edward F. Schlafly, Gregory M. Green, Douglas P. Finkbeiner, Alyssa Goodman, João Alves

Abstract: 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 acc… ▽ More 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. △ Less

Submitted 2 January, 2020; originally announced January 2020.

Comments: Accepted for publication in Astronomy & Astrophysics. Full catalog of distances available at https://doi.org/10.7910/DVN/07L7YZ or the CDS

Journal ref: A&A 633, A51 (2020)

Multiband Probabilistic Cataloging: A Joint Fitting Approach to Point Source Detection and Deblending

Authors: Richard M. Feder, Stephen K. N. Portillo, Tansu Daylan, Douglas Finkbeiner

Abstract: Probabilistic cataloging (PCAT) outperforms traditional cataloging methods on single-band optical data in crowded fields (Portillo et al. 2017). We extend our work to multiple bands, achieving greater sensitivity ($\sim$ 0.4 mag) and greater speed (500x) compared to previous single-band results. We demonstrate the effectiveness of multiband PCAT on mock data, both in terms of recovering accurate p… ▽ More Probabilistic cataloging (PCAT) outperforms traditional cataloging methods on single-band optical data in crowded fields (Portillo et al. 2017). We extend our work to multiple bands, achieving greater sensitivity ($\sim$ 0.4 mag) and greater speed (500x) compared to previous single-band results. We demonstrate the effectiveness of multiband PCAT on mock data, both in terms of recovering accurate posteriors in the catalog space, and in directly deblending sources. When applied to Sloan Digital Sky Survey (SDSS) observations of M2, taking Hubble Space Telescope data as truth, our joint fit on $r$ and $i$ band data goes $\sim0.4$ mag deeper than single-band probabilistic cataloging and has a false discovery rate less than 20\% for F606W$\leq 20$. Compared to DAOPHOT, the two-band SDSS catalog fit goes nearly 1.5 magnitudes deeper using the same data, and maintains a lower false discovery rate down to F606W$\sim 20.5$. Given recent improvements in computational speed, multiband PCAT shows promise in application to large-scale surveys and is a plausible framework for joint analysis of multi-instrument observational data. △ Less

Submitted 20 March, 2020; v1 submitted 10 July, 2019; originally announced July 2019.

Comments: 25 pages, 15 figures. Incorporated comments from referee process

Journal ref: The Astronomical Journal (2020), Volume 159, Number 4,

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

Authors: Gregory M. Green, Edward F. Schlafly, Catherine Zucker, Joshua S. Speagle, Douglas P. Finkbeiner

Abstract: 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 nearb… ▽ More 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. △ Less

Submitted 7 May, 2019; originally announced May 2019.

Comments: 43 pages, 19 figures