Machine learning for galactic archaeology: a chemistry-based neural network method for identification of accreted disc stars

Vista sencilla de ítem
dc.contributor.authorTronrud, Thorold
dc.contributor.authorTissera, Patricia B.
dc.contributor.authorGómez, Facundo A.
dc.contributor.authorGrand, Robert J. J.
dc.contributor.authorPakmor, Ruediger
dc.contributor.authorMarinacci, Federico
dc.contributor.authorSimpson, Christine M.
dc.date.accessioned2023-07-11T19:06:45Z
dc.date.available2023-07-11T19:06:45Z
dc.date.issued2022-09
dc.descriptionIndexación: Scopuses
dc.description.abstractWe 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.es
dc.identifier.citationMonthly Notices of the Royal Astronomical Society, Volume 515, Issue 3, Pages 3818 - 38371, September 2022es
dc.identifier.doi10.1093/mnras/stac2027
dc.identifier.issn0035-8711
dc.identifier.urihttps://repositorio.unab.cl/xmlui/handle/ria/51558
dc.language.isoenes
dc.publisherOxford University Presses
dc.subjectGalaxy: evolutiones
dc.subjectmethodses
dc.subjectdata analysises
dc.titleMachine learning for galactic archaeology: a chemistry-based neural network method for identification of accreted disc starses
dc.typeArtículoes
Archivos
Bloque original
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
stac2027.pdf
Tamaño:
2.41 MB
Formato:
Adobe Portable Document Format
Descripción:
TEXTO COMPLETO EN INGLÉS
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar
Colecciones
FCE - Artículos de Revista