ComPACT: Mass-redshift properties of the galaxy cluster catalogue

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