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Disentangling Milky Way Evolution with Disentangled Representation Learning, a New Tool for Chemical Tagging

30 Mar 2021, 10:00 UTC
Disentangling Milky Way Evolution with Disentangled Representation Learning, a New Tool for Chemical Tagging
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Title: Disentangled Representation Learning for Astronomical Chemical TaggingAuthors: Damien de Mijolla, Melissa Ness, Serena Viti, Adam WheelerFirst Author’s Institution: Department of Physics and Astronomy, University College LondonStatus: Accepted to the Astrophysical JournalThe question of galaxy formation and evolution is a big one in astronomy, and the Milky Way is a convenient test bed for studying this question. The Milky Way is composed largely of stars, gas, dust, and dark matter, and all of these components can be studied individually and collectively to inform our understanding of how galaxies form and evolve. Galactic archaeology is a subfield of astronomy that treats individual stars as ‘fossils,’ using them as tools to study the evolution of our Galaxy. The kinematic and chemical properties of a star hint at its ancestry. A star’s position in and motion through our Galaxy, for example, can tell us in what portion of the Galaxy it was born (e.g. thick disk, thin disk, halo, or bulge), whether it is a member of a certain cluster of stars, or whether it was part of a stellar population that was accreted by the Milky Way. The chemical composition of a star also contains a plethora of information about its history, ...

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