Galaxy clusters in the LoTSS-DR3: Catalogues and detection pipeline for diffuse radio emission

TL;DR

This paper presents an automated pipeline using AI to detect diffuse radio emission in galaxy clusters from the LoTSS-DR3 survey, enabling large-scale cataloging and analysis.

Contribution

The development of a convolutional neural network-based pipeline for pixel-level detection of diffuse radio emission in galaxy clusters from radio survey data.

Findings

Achieved 76% accuracy in identifying diffuse radio emission.

Produced a catalog of 3822 galaxy clusters with associated diffuse emission.

Demonstrated increased detection fraction with cluster mass and redshift.

Abstract

The third data release of the LOFAR Two-metre Sky Survey provides an unprecedented view of the northern sky at 144 MHz. While compact sources can be efficiently identified with automated software packages, the detection of diffuse radio emission associated with galaxy clusters still requires dedicated processing and visual inspection. Given the scale of current and forthcoming radio surveys, automated approaches based on artificial intelligence are becoming essential to the identification of the most interesting targets. We aim to develop an automated pipeline to construct a catalogue of galaxy clusters hosting diffuse radio emission from LoTSS-DR3 20arcsec images. The pipeline is designed to provide both the probability that a cluster hosts diffuse radio emission and an interpretable image of its shape and morphology. We employed Radio U-Net, a convolutional neural network optimised for image segmentation (i.e. pixel-level identification) of diffuse radio emission. To associate detected emission with individual clusters, we combined the network output with positional, mass, and redshift information from four X-ray- and Sunyaev-Zeldovich-selected cluster catalogues, resulting in a merged sample of 3822 clusters covered by the LoTSS-DR3. We produced a pixel-level segmentation map of the full LoTSS-DR3 and a quantitative indicator for the presence of diffuse emission in each cluster. This enables the selection of sub-samples with specific properties for targeted follow-up or statistical studies. As a demonstration of the first application, we identified a sub-sample of 357 clusters selected at the highest network accuracy (76%), and we showed some examples of newly detected systems. For the second, using a larger statistical sample, we verified that the detection fraction of diffuse radio sources in the four catalogues increases with the mass and redshift of the clusters. [Abridged]