http://arxiv.org/abs/2112.02072
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% for 4-component approximation. The statistical error is shown to be inferior to the systematic one related to the choice of the hadronic interaction model used for simulations.
O. Kalashev, I. Kharuk, M. Kuznetsov, et. al.
Mon, 6 Dec 21
33/61
Comments: 18 pages, 5 figures