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

TL;DR

This paper demonstrates that Bayesian neural networks significantly improve energy reconstruction accuracy in ground-based TeV astrophysics experiments, especially for high-energy cosmic ray showers.

Contribution

The study applies Bayesian neural networks to energy reconstruction in EAS experiments, showing notable improvements over standard methods.

Findings

BNNs improve energy resolution compared to standard methods

Enhancement is more significant for high-energy showers

Demonstrates effectiveness of Bayesian approaches in astrophysics data analysis

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.