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

TL;DR

This paper introduces a self-supervised, physics-aware Bayesian neural network designed for modeling galaxy emission-line kinematics, enabling efficient analysis of large astronomical datasets with uncertainty quantification.

Contribution

It presents the first application of self-supervised, physics-aware Bayesian neural networks for galaxy kinematic modeling, demonstrating accurate parameter recovery and error estimation.

Findings

Successfully models galaxy rotation curves, inclinations, and disc scale lengths.

Provides uncertainty estimates via Monte-Carlo dropout.

Matches well with literature values for CO and HI data.

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 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.