Search for Galaxy Cluster Candidates in the Cosmic Microwave Background Maps of the Planck Space Mission Using a Convolutional Neural Network Based on the Method of Tracing the Sunyaev-Zeldovich Effect

Abstract

We propose a method of searching for radio sources exhibiting the Sunyaev-Zeldovich effect in the multi-frequency emission maps from the Planck mission data using a convolutional neural network. A catalog for recognizing radio sources is compiled using the GLESP pixelation scheme at the frequencies of 100, 143, 217, 353, and 545 GHz. The quality of the proposed approach is evaluated and the quality of the dependence of model data on the S/N ratio is estimated. We show that the presented neural network approach allows the detection of sources with the Sunyaev-Zeldovich effect. The proposed method can be used to find the most likely galaxy cluster candidates at large redshifts.