Machine learning for galactic archaeology: a chemistry-based neural network method for identification of accreted disc stars
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Archivos
Fecha
2022-09
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
Oxford University Press
Nombre de Curso
Licencia CC
Licencia CC
Resumen
We develop a method ('Galactic Archaeology Neural Network', gann) based on neural network models (NNMs) to identify accreted stars in galactic discs by only their chemical fingerprint and age, using a suite of simulated galaxies from the Auriga Project. We train the network on the target galaxy's own local environment defined by the stellar halo and the surviving satellites. We demonstrate that this approach allows the detection of accreted stars that are spatially mixed into the disc. Two performance measures are defined - recovery fraction of accreted stars, frecov and the probability that a star with a positive (accreted) classification is a true-positive result, P(TP). As the NNM output is akin to an assigned probability (Pa), we are able to determine positivity based on flexible threshold values that can be adjusted easily to refine the selection of presumed-accreted stars. We find that gann identifies accreted disc stars within simulated galaxies, with high frecov and/or high P(TP). We also find that stars in Gaia-Enceladus-Sausage (GES) mass systems are over 50 per cent recovered by our NNMs in the majority (18/24) of cases. Additionally, nearly every individual source of accreted stars is detected at 10 per cent or more of its peak stellar mass in the disc. We also demonstrate that a conglomerated NNM, trained on the halo and satellite stars from all of the Auriga galaxies provides the most consistent results, and could prove to be an intriguing future approach as our observational capabilities expand. © 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.
Notas
Indexación: Scopus
Palabras clave
Galaxy: evolution, methods, data analysis
Citación
Monthly Notices of the Royal Astronomical Society, Volume 515, Issue 3, Pages 3818 - 38371, September 2022
DOI
10.1093/mnras/stac2027