Application of deep learning methods combined with physical background in wide field of view imaging atmospheric Cherenkov telescopes

Abstract

The High Altitude Detection of Astronomical Radiation (HADAR) experiment, which was constructed in Tibet, China, combines the wide-angle advantages of traditional EAS array detectors with the high-sensitivity advantages of focused Cherenkov detectors. Its objective is to observe transient sources such as gamma-ray bursts and the counterparts of gravitational waves. This study aims to utilize the latest AI technology to enhance the sensitivity of HADAR experiments. Training datasets and models with distinctive creativity were constructed by incorporating the relevant physical theories for various applications. These models can determine the type, energy, and direction of the incident particles after careful design. We obtained a background identification accuracy of 98.6%, a relative energy reconstruction error of 10.0%, and an angular resolution of 0.22\(^\circ\) in a test dataset at 10 TeV. These findings demonstrate the significant potential for enhancing the precision and dependability of detector data analysis in astrophysical research. By using 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. In addition, our experiment offers a new approach for dealing with strongly connected, scattered data.