Machine learning for galactic archaeology: a chemistry-based neural network method for identification of accreted disc stars
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
Tronrud, Thorold et al.
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2022