Radio Halo Detection in MWA Data using Deep Neural Networks and Generative Data Augmentation

TL;DR

This paper introduces a machine learning framework utilizing generative models to detect diffuse radio halos in galaxy clusters from MWA data, achieving high accuracy and rediscovering known halos while identifying new candidates.

Contribution

Developed a novel ML approach with generative data augmentation for unbiased radio halo detection, surpassing traditional pre-selection methods.

Findings

Achieved 95.93% validation accuracy with the diffusion-supported classifier.

Successfully rediscovered 75% of known halos in MGCLS and 63% in PSZ2.

Identified 11 new potential radio halo candidates in the GLEAM survey.

Abstract

Detecting diffuse radio emission, such as from halos, in galaxy clusters is crucial for understanding large-scale structure formation in the universe. Traditional methods, which rely on X-ray and Sunyaev-Zeldovich (SZ) cluster pre-selection, introduce biases that limit our understanding of the full population of diffuse radio sources. In this work, we provide a possible resolution for this astrophysical tension by developing a machine learning (ML) framework capable of unbiased detection of diffuse emission, using a limited real dataset like those from the Murchison Widefield Array (MWA). We generate for the first time radio halo images using Wasserstein Generative Adversarial Networks (WGANs) and Denoising Diffusion Probabilistic Models (DDPMs), and apply them to train a neural network classifier independent of pre-selection methods. The halo images generated by DDPMs are of higher quality than those produced by WGANs. The diffusion-supported classifier with a multi-head attention block achieved the best average validation accuracy of 95.93% over 10 runs, using 36 clusters for training and 10 for testing, without further hyperparameter tuning. Using our classifier, we rediscovered 9/12 halos (75% detection rate) from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS) Catalogue, and 5/8 halos (63% detection rate) from the Planck Sunyaev-Zeldovich Catalogue 2 (PSZ2) within the GaLactic and Extragalactic All-sky MWA (GLEAM) survey. In addition, we identify 11 potential new halos, minihalos, or candidates in the COSMOS field using XMM-chandra-detected clusters in GLEAM data. This work demonstrates the potential of ML for unbiased detection of diffuse emission and provides labeled datasets for further study.