A targeted machine learning approach for detecting diffuse radio emission with Astronomaly: Protege

TL;DR

This paper presents a machine learning pipeline combining self-supervised feature extraction and anomaly detection to efficiently identify diffuse radio emissions in galaxy clusters, addressing challenges posed by large data volumes and low surface brightness.

Contribution

It introduces a novel active learning-based approach using Bootstrap Your Own Latent and Astronomaly: Protege for detecting diffuse radio sources with minimal human labeling.

Findings

High-resolution features outperform convolved features in detection efficiency.

Protege achieves 99% accuracy in identifying diffuse sources among top candidates.

Over half of the top-ranked candidates are confirmed as cluster-related emissions.

Abstract

Diffuse radio emission in galaxy clusters, such as radio halos, relics, and mini halos, is a key tracer of non-thermal processes, turbulence, and magnetic fields within the intra-cluster medium. However, their low surface brightness, as well as contamination from compact sources and imaging artefacts, makes their detection challenging. The sheer volume of data from instruments such as the Square Kilometre Array will render traditional manual-inspection based detection methods infeasible. This paper introduces a novel machine learning approach that uses active learning to rapidly identify diffuse emission candidates from a small, optimally-selected subset of data. We apply the self-supervised deep learning algorithm Bootstrap Your Own Latent to extract features from source cutouts in the MeerKAT Galaxy Cluster Legacy Survey (MGCLS). We then pass these features through the Astronomaly: Protege anomaly detection framework to identify the final candidates. Using a human-labelled set, we evaluate our pipeline on high-resolution (~7''), convolved (15''), and combined-feature MGCLS datasets. Interestingly, the high-resolution features identify diffuse sources more efficiently than the convolved resolution, which are in turn outperformed by the combined features. Of the top 100 sources ranked by Protege, 99% exhibit diffuse characteristics, with 55% confirmed as cluster-related emission. Our work shows that Protege can identify diffuse emission with minimal human labelling effort, offering a powerful, scalable tool capable of detecting both known and novel diffuse radio sources.