Deep learning method for identifying mass composition of ultra-high-energy cosmic rays

Abstract

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.

19 pages, 5 figures

References in corpus (10)

• The Cosmic Ray Energy Spectrum Observed with the Surface Detector of the Telescope Array Experiment
• Testing Hadronic Interactions at Ultrahigh Energies with Air Showers Measured by the Pierre Auger Observatory
• Study of Ultra-High Energy Cosmic Ray Composition Using Telescope Array's Middle Drum Detector and Surface Array in Hybrid Mode
• Report on Tests and Measurements of Hadronic Interaction Properties with Air Showers
• Muon content of ultra-high-energy air showers: Yakutsk data versus simulations
• Probing the evolution of the EAS muon content in the atmosphere with KASCADE-Grande
• Signatures of the transition from galactic to extragalactic cosmic rays
• UHECR mass composition at highest energies from anisotropy of their arrival directions
• Using Deep Learning to Enhance Event Geometry Reconstruction for the Telescope Array Surface Detector
• CORSIKA Simulation of the Telescope Array Surface Detector