Applying machine learning to Galactic Archaeology: how well can we recover the origin of stars in Milky Way-like galaxies?

TL;DR

This study develops machine learning models trained on cosmological simulations to distinguish between in-situ and accreted stars in Milky Way-like galaxies, aiming to improve identification of stellar origins in galactic archaeology.

Contribution

The paper introduces ML models that effectively classify stellar origins using combined positional, kinematic, chemical, and photometric data, demonstrating applicability across different simulations.

Findings

Models achieve PR-AUC of ~0.6 in classifying stars.

Over 90% of accreted stars are retrieved beyond 5 kpc radius.

Models perform well even in in-situ dominated regions.

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 [$\alpha$/Fe]) and photometric properties. Models from all categories perform similarly well, with area under the precision-recall curve (PR-AUC) scores of $\simeq 0.6$. Beyond a galactocentric radius of $5$~kpc, models retrieve $>90\%$ of accreted stars, with a sample purity close to $60\%$, 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 minimising the contamination from in-situ stars.