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

TL;DR

This paper introduces a convolutional neural network method to identify galaxy cluster candidates via the Sunyaev-Zeldovich effect in Planck mission microwave maps, enabling detection at high redshifts.

Contribution

A novel neural network approach for detecting Sunyaev-Zeldovich sources in multi-frequency Planck data, improving galaxy cluster candidate identification.

Findings

Effective detection of Sunyaev-Zeldovich sources demonstrated

Method's performance depends on signal-to-noise ratio

Potential to identify high-redshift galaxy clusters

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.