# Robust profile decomposition for large extragalactic spectral-line surveys

## TL;DR

This paper introduces a Bayesian MCMC-based algorithm for robustly decomposing and classifying spectral-line profiles in large extragalactic radio data cubes, improving reliability and automation over traditional methods.

## Contribution

The novel algorithm enhances profile decomposition accuracy and automation in large radio surveys by using Bayesian inference and MCMC techniques, with robust error estimation.

## Key findings

- Reliable profile decomposition demonstrated on artificial data.
- Effective classification of kinematic components achieved.
- Consistent results with classical methods on real galaxy data.

## Abstract

We present a novel algorithm that is based on a Bayesian Markov Chain Monte Carlo (MCMC) technique for performing robust profile analysis of a data cube from either single-dish or interferometric radio telescopes. It fits a set of models comprised of a number of Gaussian components given by the user to individual line-of-sight velocity profiles, then compares them and finds an optimal model based on the Bayesian Inference Criteria computed for each model.The decomposed Gaussian components are then classified into bulk or non-circular motions as well as kinematically cold or warm components. The fitting based on the Bayesian MCMC technique is insensitive to initial estimates of the parameters, and suffers less from finding the global minimum in models given enough sampling points and a wide range of priors for the parameters. It is found to provide reliable profile decomposition and classification of the decomposed components in a fully automated way, together with robust error estimation of the parameters as shown by performance tests using artificial data cubes. We apply the newly developed algorithm to the HI data cubes of sample galaxies from the Local Volume HI galaxy Survey (LVHIS). We also compare the kinematically cold and warm components, and bulk velocity fields with previous analyses made in a classical method.