Application of Deep Learning Methods Combined with Physical Background in Wide Field of View Imaging Atmospheric Cherenkov Telescopes

TL;DR

This paper demonstrates how deep learning combined with physical theories can significantly improve the sensitivity and accuracy of wide field of view Cherenkov telescopes, exemplified by the HADAR experiment's enhanced detection capabilities.

Contribution

The study introduces a novel AI-based data analysis framework incorporating physical background knowledge, boosting detection accuracy and energy resolution in wide field Cherenkov telescope data.

Findings

Background identification accuracy of 98.6%

Energy reconstruction error of 10.0%

Angular resolution of 0.22 degrees at 10 TeV

Abstract

The HADAR experiment, which will be constructed in Tibet, China, combines the wide-angle advantages of traditional EAS array detectors with the high sensitivity advantages of focused Cherenkov detectors. Its physics objective is to observe transient sources such as gamma-ray bursts and counterparts of gravitational waves. The aim of this study is to utilize the latest AI technology to enhance the sensitivity of the HADAR experiment. We have built training datasets and models with distinctive creativity by incorporating relevant physical theories for various applications. They are able to determine the kind, energy, and direction of incident particles after careful design. We have obtained a background identification accuracy of 98.6%, a relative energy reconstruction error of 10.0%, and an angular resolution of 0.22-degrees in a test dataset at 10 TeV. These findings demonstrate the enormous potential for enhancing the precision and dependability of detector data analysis in astrophysical research. Thanks to deep learning techniques, the HADAR experiment's observational sensitivity to the Crab Nebula has surpassed that of MAGIC and H.E.S.S. at energies below 0.5 TeV and remains competitive with conventional narrow-field Cherenkov telescopes at higher energies. Additionally, our experiment offers a fresh approach to dealing with strongly connected scattered data.