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

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