Optimal Probabilistic Catalogue Matching for Radio Sources

TL;DR

This paper introduces a Bayesian-based automated method for cross-matching radio sources with optical/infrared counterparts, effectively handling complex morphologies and large datasets, advancing beyond traditional manual approaches.

Contribution

The paper presents a novel Bayesian hypothesis testing algorithm that models complex radio source morphologies for large-scale, automated catalogue cross-matching.

Findings

Performs well in unsupervised mode

Handles complex radio source morphologies

Scales to millions of sources

Abstract

Cross-matching catalogues from radio surveys to catalogues of sources at other wavelengths is extremely hard, because radio sources are often extended, often consist of several spatially separated components, and often no radio component is coincident with the optical/infrared host galaxy. Traditionally, the cross-matching is done by eye, but this does not scale to the millions of radio sources expected from the next generation of radio surveys. We present an innovative automated procedure, using Bayesian hypothesis testing, that models trial radio-source morphologies with putative positions of the host galaxy. This new algorithm differs from an earlier version by allowing more complex radio source morphologies, and performing a simultaneous fit over a large field. We show that this technique performs well in an unsupervised mode.