Application of Bayesian Neural Networks to Energy Reconstruction in EAS Experiments for ground-based TeV Astrophysics

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 \cite{lhaaso-ma}, 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.