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

Feder, Richard M. and Portillo, Stephen K. N. and Daylan, Tansu and Finkbeiner, Douglas (2020) Multiband Probabilistic Cataloging: A Joint Fitting Approach to Point Source Detection and Deblending. Astronomical Journal, 159 (4). Art. No. 163. ISSN 1538-3881.

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Probabilistic cataloging (PCAT) outperforms traditional cataloging methods on single-band optical data in crowded fields. We extend our work to multiple bands, achieving greater sensitivity (~0.4 mag) and greater speed (500×) compared to previous single-band results. We demonstrate the effectiveness of multiband PCAT on mock data, in terms of both recovering accurate posteriors in the catalog space and 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 ~0.4 mag deeper than single-band probabilistic cataloging and has a false discovery rate less than 20% for F606W ≤ 20. Compared to DAOPHOT, the two-band SDSS catalog fit goes nearly 1.5 mag deeper using the same data and maintains a lower false discovery rate down to F606W ~ 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.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Feder, Richard M.0000-0002-9330-8738
Portillo, Stephen K. N.0000-0001-8132-8056
Daylan, Tansu0000-0002-6939-9211
Finkbeiner, Douglas0000-0003-2808-275X
Additional Information:© 2020 The American Astronomical Society. Received 2019 July 10; revised 2020 February 5; accepted 2020 February 9; published 2020 March 19. R.M.F. is supported by the California Institute of Technology and was also supported by the Harvard-Smithsonian Center for Astrophysics. S.K.N.P. acknowledges support from a Sir James Lougheed Award of Distinction, as well as the DIRAC Institute in the Department of Astronomy at the University of Washington. The DIRAC Institute is supported through generous gifts from the Charles and Lisa Simonyi Fund for Arts and Sciences and the Washington Research Foundation. T.D. acknowledges support from MIT's Kavli Institute as a Kavli postdoctoral fellow. We thank Aneta Siemiginowska, Vinay Kashyap, and Josh Speagle for useful comments throughout the course of this work. The computations in this paper were run on the Odyssey cluster supported by the FAS Division of Science, Research Computing Group at Harvard University. This research has made use of NASA's Astrophysics Data System. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS website is SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration, including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University. Software: Lion (Portillo et al. 2017), crowdsource (Schlafly et al. 2018), NumPy, Matplotlib, CBLAS, SciPy.
Funding AgencyGrant Number
Harvard-Smithsonian Center for AstrophysicsUNSPECIFIED
University of WashingtonUNSPECIFIED
Charles and Lisa Simonyi Fund for Arts and SciencesUNSPECIFIED
Washington Research FoundationUNSPECIFIED
Massachusetts Institute of Technology (MIT)UNSPECIFIED
Alfred P. Sloan FoundationUNSPECIFIED
Department of Energy (DOE)UNSPECIFIED
Participating InstitutionsUNSPECIFIED
Subject Keywords:Catalogs ; Star counts ; Sky surveys ; Globular star clusters ; Bayesian statistics ; Hierarchical models
Issue or Number:4
Classification Code:Unified Astronomy Thesaurus concepts: Catalogs (205); Star counts (1568); Sky surveys (1464); Globular star clusters (656); Bayesian statistics (1900); Hierarchical models (1925)
Record Number:CaltechAUTHORS:20200127-155716079
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Official Citation:Richard M. Feder et al 2020 AJ 159 163
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100957
Deposited By: Tony Diaz
Deposited On:28 Jan 2020 18:03
Last Modified:19 Mar 2020 19:57

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