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, P. F. Hopkins

Research output: Contribution to journalArticle

1 Scopus citations

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.

Original languageEnglish (US)
Article number201936866
JournalAstronomy and Astrophysics
Volume636
DOIs
StatePublished - Apr 1 2020

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

Keywords

  • Catalogs
  • Galaxy: halo
  • Galaxy: kinematics and dynamics
  • Methods: data analysis
  • Solar neighborhood

Fingerprint Dive into the research topics of 'Cataloging accreted stars within Gaia DR2 using deep learning'. Together they form a unique fingerprint.

  • Cite this

    Ostdiek, B., Necib, L., Cohen, T., Freytsis, M., Lisanti, M., Garrison-Kimmmel, S., Wetzel, A., Sanderson, R. E., & Hopkins, P. F. (2020). Cataloging accreted stars within Gaia DR2 using deep learning. Astronomy and Astrophysics, 636, [201936866]. https://doi.org/10.1051/0004-6361/201936866