Deep learning method for identifying mass composition of ultra-high-energy cosmic rays
This addresses the challenge of accurately determining cosmic ray composition for astrophysics research, representing a strong specific gain in this domain.
The paper tackles the problem of identifying the mass composition of ultra-high-energy cosmic rays by introducing a deep learning method using two neural networks, achieving an error of 7% on Monte-Carlo data for a 4-component approximation.
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. We also discuss the problems of applying the developed method to the experimental data, and the way they can be resolved.