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.

摘要——我们提出了一种使用卷积神经网络在普朗克任务数据的多频发射图中搜索表现出 Sunyaev-Zeldovich 效应的无线电源的方法。使用 GLESP 像素化方案在 100、143、217、353 和 545 GHz 频率下编译用于识别无线电源的目录。评估所提出方法的质量，并估计模型数据对S / N比的依赖性的质量。我们表明，所提出的神经网络方法允许检测具有 Sunyaev-Zeldovich 效应的源。所提出的方法可用于在大红移处找到最可能的星系团候选者。