Application of a Bayesian Method to Absorption Spectral-Line Finding in Simulated ASKAP Data

TL;DR

This paper introduces a Bayesian Monte Carlo method for detecting and fitting HI absorption lines in radio astronomy data, demonstrating its reliability on simulated ASKAP data without data smoothing.

Contribution

The paper presents a novel Bayesian multi-nested sampling approach for simultaneous detection and parametrization of HI absorption lines in radio data.

Findings

Reliable detection of absorption lines in low S/N data.

Quantitative significance assessment using Bayesian evidence.

Effective model selection for line-profile fitting.

Abstract

The large spectral bandwidth and wide field of view of the Australian SKA Pathfinder radio telescope will open up a completely new parameter space for large extragalactic HI surveys. Here we focus on identifying and parametrising HI absorption lines which occur in the line of sight towards strong radio continuum sources. We have developed a method for simultaneously finding and fitting HI absorption lines in radio data by using multi-nested sampling, a Bayesian Monte Carlo algorithm. The method is tested on a simulated ASKAP data cube, and is shown to be reliable at detecting absorption lines in low signal-to-noise data without the need to smooth or alter the data. Estimation of the local Bayesian evidence statistic provides a quantitative criterion for assigning significance to a detection and selecting between competing analytical line-profile models.