A self-supervised, physics-aware, Bayesian neural network architecture
  for modelling galaxy emission-line kinematics
  In the upcoming decades large facilities, such as the SKA, will provide
resolved observations of the kinematics of millions of galaxies. In order to
assist in the timely exploitation of these vast datasets we explore the use of
a self-supervised, physics aware neural network capable of Bayesian kinematic
modelling of galaxies. We demonstrate the network's ability to model the
kinematics of cold gas in galaxies with an emphasis on recovering physical
parameters and accompanying modelling errors. The model is able to recover
rotation curves, inclinations and disc scale lengths for both CO and HI data
which match well with those found in the literature. The model is also able to
provide modelling errors over learned parameters thanks to the application of
quasi-Bayesian Monte-Carlo dropout. This work shows the promising use of
machine learning, and in particular self-supervised neural networks, in the
context of kinematically modelling galaxies. This work represents the first
steps in applying such models for kinematic fitting and we propose that
variants of our model would seem especially suitable for enabling emission-line
science from upcoming surveys with e.g. the SKA, allowing fast exploitation of
these large datasets.
