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