Sante, A, Font, AS, Ortega-Martorell, S, Olier, I and McCarthy, IG (2024) Applying machine learning to Galactic Archaeology: how well can we recover the origin of stars in Milky Way-like galaxies? Monthly Notices of the Royal Astronomical Society, 531 (4). pp. 4363-4382. ISSN 0035-8711

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Abstract

We present several machine learning (ML) models developed to efficiently separate stars formed in situ in Milky Way-type galaxies from those that were formed externally and later accreted. These models, which include examples from artificial neural networks, decision trees, and dimensionality reduction techniques, are trained on a sample of disc-like, Milky Way-mass galaxies drawn from the ARTEMIS cosmological hydrodynamical zoom-in simulations. We find that the input parameters which provide an optimal performance for these models consist of a combination of stellar positions, kinematics, chemical abundances ([Fe/H] and [α/Fe]), and photometric properties. Models from all categories perform similarly well, with area under the precision–recall curve (PR-AUC) scores of 0.6. Beyond a galactocentric radius of 5 kpc, models retrieve > 90 per cent of accreted stars, with a sample purity close to 60 per cent, however the purity can be increased by adjusting the classification threshold. For one model, we also include host galaxy-specific properties in the training, to account for the variability of accretion histories of the hosts, however this does not lead to an improvement in performance. The ML models can identify accreted stars even in regions heavily dominated by the in-situ component (e.g. in the disc), and perform well on an unseen suite of simulations (the AURIGA simulations). The general applicability bodes well for application of such methods on observational data to identify accreted substructures in the Milky Way without the need to resort to selection cuts for minimizing the contamination from in-situ stars.

Item Type:	Article
Uncontrolled Keywords:	0201 Astronomical and Space Sciences; Astronomy & Astrophysics
Subjects:	Q Science > QB Astronomy Q Science > QC Physics
Divisions:	Astrophysics Research Institute
Publisher:	Oxford University Press (OUP)
SWORD Depositor:	A Symplectic
Date Deposited:	01 Jul 2024 13:43
Last Modified:	01 Jul 2024 13:45
DOI or ID number:	10.1093/mnras/stae1398
URI:	https://researchonline.ljmu.ac.uk/id/eprint/23672

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