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.
Fecha de publicación:
9
2022