  Numerous studies have demonstrated the ability of Convolutional Neural
Networks (CNNs) to classify large numbers of galaxies in a manner which mimics
the expertise of astronomers. Such classifications are not always physically
motivated, however, such as categorising galaxies by their morphological types.
In this work, we consider the use of CNNs to classify simulated S0 galaxies
based on fundamental physical properties. In particular, we undertake two
investigations: (1) the classification of simulated S0 galaxies into three
distinct evolutionary paths (isolated, tidal interaction in a group halo, and
Spiral-Spiral merger), and (2) the prediction of the mass ratio for the S0s
formed via mergers. To train the CNNs, we first run several hundred N-body
simulations to model the formation of S0s under idealised conditions; and then
we build our training datasets by creating images of stellar density and two
dimensional kinematic maps for each simulated S0. Our trained networks have
remarkable accuracies exceeding 99% when classifying the S0 formation pathway.
For the case of predicting merger mass ratios, the mean predictions are
consistent with the true values to within roughly one standard deviation across
the full range of our data. Our work demonstrates the potential of CNNs to
classify galaxies by the fundamental physical properties which drive their
evolution.
