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Cataloging Accreted Stars within Gaia DR2 using Deep Learning

Ostdiek, B. and Necib, L. and Cohen, T. and Freytsis, M. and Lisanti, M. and Garrison-Kimmel, S. and Wetzel, A. and Sanderson, R. E. and Hopkins, P. F. (2020) Cataloging Accreted Stars within Gaia DR2 using Deep Learning. Astronomy and Astrophysics, 636 . Art. No. A75. ISSN 0004-6361. https://resolver.caltech.edu/CaltechAUTHORS:20190726-080442014

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Abstract

Aims. The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ. Traditional selection methods that have been used to identify accreted stars typically rely on full 3D velocity, metallicity information, or both, which significantly reduces the number of classifiable stars. The approach advocated here is applicable to a much larger portion of Gaia DR2. Methods. A method known as “transfer learning” is shown to be effective through extensive testing on a set of mock Gaia catalogs that are based on the FIRE cosmological zoom-in hydrodynamic simulations of Milky Way-mass galaxies. The machine is first trained on simulated data using only 5D kinematics as inputs and is then further trained on a cross-matched Gaia/RAVE data set, which improves sensitivity to properties of the real Milky Way. Results. The result is a catalog that identifies ∼767 000 accreted stars within Gaia DR2. This catalog can yield empirical insights into the merger history of the Milky Way and could be used to infer properties of the dark matter distribution.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1051/0004-6361/201936866DOIArticle
https://arxiv.org/abs/1907.06652arXivDiscussion Paper
ORCID:
AuthorORCID
Necib, L.0000-0003-2806-1414
Garrison-Kimmel, S.0000-0002-4655-8128
Wetzel, A.0000-0003-0603-8942
Sanderson, R. E.0000-0003-3939-3297
Hopkins, P. F.0000-0003-3729-1684
Additional Information:© 2020 ESO. Article published by EDP Sciences. Received 7 October 2019; Accepted 21 January 2020; Published online 21 April 2020. We are grateful to Ben Farr and Graham Kribs for useful discussions. This work utilized the University of Oregon Talapas high performance computing cluster. BO and TC are supported by US Department of Energy (DOE), under grant number DE-SC0011640. LN is supported by the DOE under Award Number DE-SC0011632, and the Sherman Fairchild fellowship. MF is supported by the Zuckerman STEM Leadership Program and in part by the DOE under grant number DE-SC0011640. ML is supported by the DOE under contract DE-SC0007968 and the Cottrell Scholar Program through the Research Corporation for Science Advancement. AW is supported by NASA, through ATP grant 80NSSC18K1097 and HST grants GO-14734 and AR-15057 from STScI, and a Hellman Fellowship from UC Davis. SGK and PFH are supported by an Alfred P. Sloan Research Fellowship, NSF Collaborative Research Grant #1715847 and CAREER grant #1455342, and NASA grants NNX15AT06G, JPL 1589742, 17-ATP17-0214. Numerical simulations were run on the Caltech compute cluster “Wheeler”, allocations from XSEDE TG-AST130039 and PRAC NSF.1713353 supported by the NSF, and NASA HEC SMD-16-7592. This work was performed in part at Aspen Center for Physics, which is supported by National Science Foundation grant PHY-1607611. We also are grateful for the support from the 2018 CERN-Korea TH Institute. This research was supported by the Munich Institute for Astro- and Particle Physics (MIAPP) of the DFG cluster of excellence “Origin and Structure of the Universe”. This research was supported in part by the National Science Foundation under Grant No. NSF PHY-1748958. RES thanks Nick Carriero, Ian Fisk, and Dylan Simon of the Scientific Computing Core at the Flatiron Institute for their support of the infrastructure housing the synthetic surveys and simulations used for this work. This work has made use of data from the European Space Agency (ESA) mission Gaia (http://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. Funding for RAVE has been provided by: the Australian Astronomical Observatory; the Leibniz-Institut fuer Astrophysik Potsdam (AIP); the Australian National University; the Australian Research Council; the French National Research Agency; the German Research Foundation (SPP 1177 and SFB 881); the European Research Council (ERC-StG 240271 Galactica); the Istituto Nazionale di Astrofisica at Padova; The Johns Hopkins University; the National Science Foundation of the USA (AST-0908326); the W. M. Keck foundation; the Macquarie University; the Netherlands Research School for Astronomy; the Natural Sciences and Engineering Research Council of Canada; the Slovenian Research Agency; the Swiss National Science Foundation; the Science & Technology Facilities Council of the UK; Opticon; Strasbourg Observatory; and the Universities of Groningen, Heidelberg and Sydney. The RAVE web site is at https://www.rave-survey.org.
Group:Astronomy Department, TAPIR, Walter Burke Institute for Theoretical Physics
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0011640
Department of Energy (DOE)DE-SC0011632
Sherman Fairchild FoundationUNSPECIFIED
Zuckerman STEM Leadership ProgramUNSPECIFIED
Department of Energy (DOE)DE-SC0007968
Cottrell Scholar of Research CorporationUNSPECIFIED
NASA80NSSC18K1097
NASA Hubble FellowshipGO-14734
NASA Hubble FellowshipAR-15057
University of California, DavisUNSPECIFIED
Alfred P. Sloan FoundationUNSPECIFIED
NSFAST-1715847
NSFAST-1455342
NASANNX15AT06G
JPL1589742
JPL17-ATP17-0214
NSFTG-AST130039
NSFPRAC-1713353
NASASMD-16-7592
NSFPHY-1607611
CERN-Korea TH InstituteUNSPECIFIED
Munich Institute for Astro- and Particle Physics (MIAPP)UNSPECIFIED
NSFPHY-1748958
Flatiron InstituteUNSPECIFIED
Gaia Multilateral AgreementUNSPECIFIED
Australian Astronomical Observatory (AAO)UNSPECIFIED
Leibniz-Institut fuer Astrophysik Potsdam (AIP)UNSPECIFIED
Australian National UniversityUNSPECIFIED
Australian Research CouncilUNSPECIFIED
Agence Nationale pour la Recherche (ANR)UNSPECIFIED
Deutsche Forschungsgemeinschaft (DFG)SPP 1177
Deutsche Forschungsgemeinschaft (DFG)SFB 881
European Research Council (ERC)240271
Istituto Nazionale di Astrofisica (INAF)UNSPECIFIED
Johns Hopkins UniversityUNSPECIFIED
NSFAST-0908326
W. M. Keck FoundationUNSPECIFIED
Macquarie UniversityUNSPECIFIED
Nederlandse Onderzoekschool voor de Astronomie (NOVA)UNSPECIFIED
Natural Sciences and Engineering Research Council of Canada (NSERC)UNSPECIFIED
Slovenian Research AgencyUNSPECIFIED
Swiss National Science Foundation (SNSF)UNSPECIFIED
Science and Technology Facilities Council (STFC)UNSPECIFIED
OpticonUNSPECIFIED
Strasbourg ObservatoryUNSPECIFIED
University of GroningenUNSPECIFIED
University of HeidelbergUNSPECIFIED
University of SydneyUNSPECIFIED
Subject Keywords:Galaxy: kinematics and dynamics – Galaxy: halo – solar neighborhood – catalogs – methods: data analysis
Record Number:CaltechAUTHORS:20190726-080442014
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190726-080442014
Official Citation:Cataloging accreted stars within Gaia DR2 using deep learning. B. Ostdiek, L. Necib, T. Cohen, M. Freytsis, M. Lisanti, S. Garrison-Kimmmel, A. Wetzel, R. E. Sanderson and P. F. Hopkins. A&A, 636 (2020) A75 DOI: https://doi.org/10.1051/0004-6361/201936866
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97431
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:26 Jul 2019 17:41
Last Modified:21 Apr 2020 17:06

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