Deep learning method for identifying mass composition of ultra-high-energy cosmic rays. (1st May 2022)

• Record Type: Journal Article
• Authors: Kalashev, O.; Kharuk, I.; Kuznetsov, M.; Rubtsov, G.; Sako, T.; Tsunesada, Y.; Zhezher, Ya.
• Is Part Of: Journal of instrumentation. Volume 17:Number 5(2022)
• Publication Date: 2022-05-01
• Subjects: Analysis and statistical methods -- Data analysis -- Pattern recognition, cluster finding, calibration and fitting methods; Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/17/05/P05008
• Languages: English
• ISSNs: 1748-0221

Abstract

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.