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We present a method to analyse arrival directions of ultra-high-energy cosmicrays (UHECRs) using a classifier defined by a deep convolutional neural networktrained on a HEALPix grid. To illustrate a high effectiveness of the method, weemploy it to estimate prospects of detecting a large-scale anisotropy of UHECRsinduced by a nearby source with an (orbital) detector having a uniform exposureof the celestial sphere and compare the results with our earlier calculationsbased on the angular power spectrum. A minimal model for extragalactic cosmicrays and neutrinos by Kachelrie{\ss}, Kalashev, Ostapchenko and Semikoz (2017)is assumed for definiteness and nearby active galactic nuclei Centaurus A, M82,NGC 253, M87 and Fornax A are considered as possible sources of UHECRs. Wedemonstrate that the proposed method drastically improves sensitivity of anexperiment by decreasing the minimal required amount of detected UHECRs or theminimal detectable fraction of from-source events several times compared to theapproach based on the angular power spectrum. We also test robustness of theneural networks against different models of the large-scale Galactic magneticfields and variations of the mass composition of UHECRs, and considersituations when there are two nearby sources or the dominating source is notknown a~priori. In all cases, the neural networks demonstrate good performanceunless the test models strongly deviate from those used for training. Themethod can be readily applied to the analysis of data of the Telescope Array,the Pierre Auger Observatory and other cosmic ray experiments.

Identifying nearby sources of ultra-high-energy cosmic rays with deep learning