Identifying mass composition of ultra-high-energy cosmic rays using deep learning
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We introduce a novel method for identifying the mass composition of ultra-high-energy cosmic rays using deep learning. The key idea of the method is to use a chain of two neural networks. The first network predicts the type of a primary particle for individual events, while the second infers the mass composition of an ensemble of events. We apply this method to the Monte-Carlo data for the Telescope Array Surface Detectors readings, on which it yields an unprecedented low error of 7 error is shown to be inferior to the systematic one related to the choice of the hadronic interaction model used for simulations.
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