  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 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, 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, 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 recovery fraction and/or high
P(TP). We also find that stars in Gaia-Enceladus-Sausage (GES) mass systems are
over 50% recovered by our NNMs in the majority (18/24) of cases. Additionally,
nearly every individual source of accreted stars is detected at 10% 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.
