A Deep Learning Approach for Characterizing Major Galaxy Mergers
  Fine-grained estimation of galaxy merger stages from observations is a key
problem useful for validation of our current theoretical understanding of
galaxy formation. To this end, we demonstrate a CNN-based regression model that
is able to predict, for the first time, using a single image, the merger stage
relative to the first perigee passage with a median error of 38.3 million years
(Myrs) over a period of 400 Myrs. This model uses no specific dynamical
modeling and learns only from simulated merger events. We show that our model
provides reasonable estimates on real observations, approximately matching
prior estimates provided by detailed dynamical modeling. We provide a
preliminary interpretability analysis of our models, and demonstrate first
steps toward calibrated uncertainty estimation.
