ComPACT: Mass-Redshift Properties of the galaxy cluster catalogue

TL;DR

This paper evaluates the ComPACT galaxy cluster catalogue created with machine learning, confirming its clusters, estimating their properties, and highlighting its contribution to high-redshift, high-mass cluster detection.

Contribution

It presents a new SZ-selected galaxy cluster catalogue using convolutional neural networks, including 116 new redshifts, 158 new masses, and discovery of five massive clusters at high redshift.

Findings

Confirmed approximately 60% of candidates as galaxy clusters.

Redshifts range from 0.007 to 1.7, with 116 new measurements.

Masses range from 0.25 to 13.1 x 10^14 M_sun, including 158 new determinations.

Abstract

Machine-learning methods are increasingly applied to astronomical surveys, providing powerful tools for detecting and studying galaxy clusters. We investigate the mass-redshift properties and completeness of the ComPACT galaxy cluster catalogue, constructed using a convolutional neural network applied to publicly available combined ACT+Planck maps. The ComPACT catalogue contains 2,962 SZ-selected galaxy cluster candidates. We confirm clusters by estimating redshifts using literature information and photometric techniques based on DESI Legacy Imaging Surveys data. Cluster masses are derived from ACT+Planck and Planck Compton-y maps via SZ scaling relations. The completeness is assessed using simulated cluster injections into real microwave maps. We confirm approximately $\sim$60 % of the ComPACT candidates as galaxy clusters. The redshifts span the range $0.007 < z < 1.7$, including approximately 116 new measurements. Masses are obtained for 56 % of the sample, covering the range $(0.25 - 13.1) \times 10^{14} ~M_\odot$ and including 158 new mass determinations. We identify five previously unreported massive clusters ($M_{500c} > 6 \times 10^{14}~M_\odot$) at $z > 0.7$, increasing the known population of such systems by approximately 10 %. The ComPACT catalogue expands the SZ-selected Planck-like cluster population, especially at high redshift and high mass, demonstrating the effectiveness of deep-learning approaches for cluster detection in microwave data.