A self-supervised, physics-aware, Bayesian neural network architecture for modelling galaxy emission-line kinematics

Monthly Notices of the Royal Astronomical Society, vol. 503, pp. 574–585

Abstract

ABSTRACT 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 data sets, 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 H i 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 data sets.

Authors 4

1. James M Dawson corresponding

   Cardiff University

   Affiliation as printed

   Cardiff University, School of Physics and Astronomy, The Parade, Cardiff CF24 3AA, UK

2. Timothy A. Davis

   Cardiff University

   Affiliation as printed

   Cardiff University, School of Physics and Astronomy, The Parade, Cardiff CF24 3AA, UK

3. Edward L Gomez

   Las Cumbres Observatory Global Telescope Network · Cardiff University

   Affiliation as printed

   Cardiff University, School of Physics and Astronomy, The Parade, Cardiff CF24 3AA, UK

   Las Cumbres Observatory, Suite 102, 6740 Cortona Dr, Goleta, CA 93117, USA

4. Justus Schock Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Templergraben 55, D-52062 Aachen, Germany