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Unveiling the distinct formation pathways of the inner and outer discs of the Milky Way with Bayesian Machine Learning by Ioana Ciucă; Daisuke Kawata; Andrea Miglio; Guy R Davies; Robert J J Grand is a Physics and Astronomy article available to read on EtoBox.

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## ABSTRACT We develop a Bayesian Machine Learning framework called BINGO (Bayesian INference for Galactic archaeOlogy) centred around a Bayesian neural network. After being trained on the Apache Point Observatory Galactic Evolution Experiment (APOGEE) and Kepler asteroseismic age data, BINGO is used to obtain precise relative stellar age estimates with uncertainties for the APOGEE stars. We carefully construct a training set to minimize bias and apply BINGO to a stellar population that is similar to our training set. We then select the 17 305 stars with ages from BINGO and reliable kinematic properties obtained from Gaia DR2. By combining the age and chemo-kinematical information, we dissect the Galactic disc stars into three components, namely the thick disc (old, high-[α/Fe], [α/Fe] ≳ 0.12), the thin disc (young, low-[α/Fe]), and the Bridge, which is a region between the thick and thin discs. Our results indicate that the thick disc formed at an early epoch only in the inner region, and the inner disc smoothly transforms to the thin disc. We found that the outer disc follows a different chemical evolution pathway from the inner disc. The outer metal-poor stars only start forming

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Author

Ioana Ciucă; Daisuke Kawata; Andrea Miglio; Guy R Davies; Robert J J Grand

Publisher

Oxford University Press (OUP)

Published

2021

Language

EN

Field

Physics and Astronomy (Physical Sciences)