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Chasing Accreted Structures within Gaia DR2 using Deep Learning

Necib, Lina and Ostdiek, Bryan and Lisanti, Mariangela and Cohen, Timothy and Freytsis, Marat and Garrison-Kimmel, Shea (2020) Chasing Accreted Structures within Gaia DR2 using Deep Learning. Astrophysical Journal, 903 (1). Art. No. 25. ISSN 1538-4357. https://resolver.caltech.edu/CaltechAUTHORS:20200203-082604510

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Abstract

In previous work, we developed a deep neural network classifier that only relies on phase-space information to obtain a catalog of accreted stars based on the second data release of Gaia (DR2). In this paper, we apply two clustering algorithms to identify velocity substructure within this catalog. We focus on the subset of stars with line-of-sight velocity measurements that fall in the range of Galactocentric radii r ∈ [6.5, 9.5] kpc and vertical distances |z|<3 kpc. Known structures such as Gaia Enceladus and the Helmi stream are identified. The largest previously unknown structure, Nyx, is a vast stream consisting of at least 200 stars in the region of interest. This study displays the power of the machine-learning approach by not only successfully identifying known features but also discovering new kinematic structures that may shed light on the merger history of the Milky Way.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/1538-4357/abb814DOIArticle
https://arxiv.org/abs/1907.07681arXivDiscussion Paper
ORCID:
AuthorORCID
Necib, Lina0000-0003-2806-1414
Ostdiek, Bryan0000-0002-0376-6461
Lisanti, Mariangela0000-0002-8495-8659
Cohen, Timothy0000-0002-7040-3038
Freytsis, Marat0000-0002-6427-2895
Garrison-Kimmel, Shea0000-0002-4655-8128
Additional Information:© 2020 The American Astronomical Society. Received 2020 April 23; revised 2020 August 28; accepted 2020 September 12; published 2020 October 29. We thank G. Brova, P. Hopkins, E. Kirby, R. Sanderson, and A. Wetzel for helpful discussions. This work was performed in part at Aspen Center for Physics, which is supported by National Science Foundation grant PHY-1607611. 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. L.N. is supported by the DOE under Award Number DESC0011632, the Sherman Fairchild fellowship, the California Presidential fellowship, and a Carnegie Fellowship in Theoretical Astrophysics. B.O. and T.C. are supported by the US Department of Energy under grant No. DE-SC0011640. M.L. is supported by the DOE under award number DESC0007968 and the Cottrell Scholar Program through the Research Corporation for Science Advancement. M.F. is supported by the Zuckerman STEM Leadership Program and in part by the DOE under grant No. DE-SC0011640. S.G.K. is 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. 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. Software: This analysis made use of Astropy (Price-Whelan et al. 2018), Galpy (Bovy 2015), Matplotlib (Hunter 2007), NumPy (van der Walt et al. 2011), and Scikit-Learn (Pedregosa et al. 2011). The neural network used for tagging the accreted stars was implemented in Keras (Chollet 2015) with the TensorFlow backend (Abadi et al. 2015). The network was trained using Adam (Kingma & Ba 2014) to minimize the binary cross0entropy loss.
Group:TAPIR, Walter Burke Institute for Theoretical Physics
Funders:
Funding AgencyGrant Number
NSFPHY-1607611
Munich Institute for Astro- and Particle Physics (MIAPP)UNSPECIFIED
Deutsche Forschungsgemeinschaft (DFG)UNSPECIFIED
NSFPHY-1748958
Department of Energy (DOE)DE-SC0011632
Sherman Fairchild FoundationUNSPECIFIED
Department of Energy (DOE)DE-SC0011640
Department of Energy (DOE)DE-SC0007968
Cottrell Scholar of Research CorporationUNSPECIFIED
Zuckerman STEM Leadership ProgramUNSPECIFIED
Alfred P. Sloan FoundationUNSPECIFIED
NSFAST-1715847
NSFAST-1455342
NASANNX15AT06G
JPL1589742
JPL17-ATP17-0214
Gaia Multilateral AgreementUNSPECIFIED
Subject Keywords:Milky Way dynamics ; Galaxy dynamics ; Astrometry ; Neural networks ; Star clusters
Issue or Number:1
Classification Code:Unified Astronomy Thesaurus concepts: Milky Way dynamics (1051); Galaxy dynamics (591); Astrometry (80); Neural networks (1933); Star clusters (1567)
Record Number:CaltechAUTHORS:20200203-082604510
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200203-082604510
Official Citation:Lina Necib et al 2020 ApJ 903 25
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101049
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:03 Feb 2020 16:45
Last Modified:30 Oct 2020 19:24

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