Unveiling the distinct formation pathways of the inner and outer discs of the Milky Way with Bayesian Machine Learning

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

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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 APOGEE and \emph{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 minimise 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 \textit{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-[$\alpha$/Fe], [$\alpha$/Fe] $\gtrsim$ 0.12), the thin disc (young, low-[$\alpha$/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 after the compact thick disc phase has completed and the star-forming gas disc extended outwardly with metal-poor gas accretion. We found that in the Bridge region the range of [Fe/H] becomes wider with decreasing age, which suggests that the Bridge region corresponds to the transition phase from the smaller chemically well-mixed thick to a larger thin disc with a metallicity gradient.
Original languageEnglish
Pages (from-to)1-12
JournalMonthly Notices of the Royal Astronomical Society
Issue number2
Early online date8 Mar 2021
Publication statusE-pub ahead of print - 8 Mar 2021

Bibliographical note

ACKNOWLEDGEMENTS: We thank our anonymous referee for their helpful comments to improve the manuscript. IC and DK acknowledge the support of
the UK’s Science and Technology Facilities Council (STFC Grant ST/N000811/1 and ST/S000216/1). IC is also grateful for the STFC Doctoral Training Partnerships Grant (ST/N504488/1). IC thanks the LSSTC Data Science Fellowship Program, where their time as a Fellow has benefited this work. AM acknowledges support from the ERC Consolidator Grant funding scheme (project ASTEROCHRONOMETRY, grant agreement number 772293). GRD has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (CartographY GA. 804752). This work has made use of data from the European Space Agency (ESA) mission
Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https:// 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. This work was inspired from our numerical simulation studies used the UCL facility Grace and the Cambridge Service for Data Driven Discovery (CSD3), part of which is operated by the University of Cambridge Research Computing on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). The DiRAC component of CSD3 was funded by BEIS capital funding via STFC capital grants ST/P002307/1 and ST/R002452/1 and STFC operations grant ST/R00689X/1. DiRAC is part of the National e-Infrastructure.


  • Galaxy: abundances
  • Galaxy: formation
  • asteroseismology
  • astro-ph.GA


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