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Application of deep learning methods combined with physical background in wide field of view imaging atmospheric Cherenkov telescopes
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作者 Ao-Yan Cheng Hao Cai +25 位作者 Shi Chen Tian-Lu Chen Xiang Dong You-Liang Feng Qi Gao Quan-Bu Gou Yi-Qing Guo Hong-Bo Hu Ming-Ming Kang Hai-Jin Li Chen Liu Mao-Yuan Liu Wei Liu Fang-Sheng Min Chu-Cheng Pan Bing-Qiang Qiao Xiang-Li Qian Hui-Ying Sun Yu-Chang Sun Ao-Bo Wang Xu Wang Zhen Wang Guang-Guang Xin Yu-Hua Yao Qiang Yuan Yi Zhang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2024年第4期208-220,共13页
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... 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°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. 展开更多
关键词 VHE gamma-ray astronomy HADAR Deep learning Convolutional neural networks
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Experimental search for dark matter in China 被引量:1
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作者 Li Zhao Jianglai Liu 《Frontiers of physics》 SCIE CSCD 2020年第4期121-131,共11页
The nature of dark matter is one of the greatest mysteries in modern physics and astronomy.A wide variety of experiments have been carried out worldwide to search for the evidence of particle dark matter.Chinese physi... The nature of dark matter is one of the greatest mysteries in modern physics and astronomy.A wide variety of experiments have been carried out worldwide to search for the evidence of particle dark matter.Chinese physicists started experimental search for dark matter about ten years ago,and have produced results with high scientific impact.In this paper,we present an overview of the dark matter program in China,and discuss recent results and future directions. 展开更多
关键词 dark matter weakly interacting massive particle(WIMP) direct detection indirect de-tection XENON GERMANIUM
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