Application of Bayesian neural networks to energy reconstruction in EAS experiments for ground-based TeV astrophysics

Affiliation of Author(s):电子电气与物理学院

Journal:Journal of Instrumentation

Funded by:省、自治区、直辖市科技项目

Abstract:A toy detector array is designed to detect a shower generated by the interaction between a TeV cosmic ray and the atmosphere. In the present paper, the primary energies of showers detected by the detector array are reconstructed with the algorithm of Bayesian neural networks (BNNs) and a standard method like the LHAASO experiment [1], respectively. Compared to the standard method, the energy resolutions are significantly improved using the BNNs. And the improvement is more obvious for the high energy showers than the low energy ones.

Indexed by:Journal paper

Document Code:8432

Volume:11

Page Number:07006

ISSN No.:1748-0221

Translation or Not:no

Date of Publication:2016-07-12