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| DOI | 10.1007/s41365-024-01448-8 |
| Application of deep learning methods combined with physical background in wide field of view imaging atmospheric Cherenkov telescopes | |
| 发表日期 | 2024 |
| ISSN | 1001-8042 |
| EISSN | 2210-3147 |
| 起始页码 | 35 |
| 结束页码 | 4 |
| 卷号 | 35期号:4 |
| 英文摘要 | 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 degrees\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>\circ$$\end{document} 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. |
| 英文关键词 | VHE gamma-ray astronomy; HADAR; Deep learning; Convolutional neural networks |
| 语种 | 英语 |
| WOS研究方向 | Nuclear Science & Technology ; Physics |
| WOS类目 | Nuclear Science & Technology ; Physics, Nuclear |
| WOS记录号 | WOS:001232696400007 |
| 来源期刊 | NUCLEAR SCIENCE AND TECHNIQUES
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/299050 |
| 作者单位 | Wuhan University; Yunnan University; Tibet University; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Sichuan University; Shandong Management University; Shanghai Jiao Tong University; Chongqing University; Chinese Academy of Sciences; Nanjing Institute of Astronomical Optics & Technology, NAOC, CAS; Purple Mountain Observatory, CAS |
| 推荐引用方式 GB/T 7714 | . Application of deep learning methods combined with physical background in wide field of view imaging atmospheric Cherenkov telescopes[J],2024,35(4). |
| APA | (2024).Application of deep learning methods combined with physical background in wide field of view imaging atmospheric Cherenkov telescopes.NUCLEAR SCIENCE AND TECHNIQUES,35(4). |
| MLA | "Application of deep learning methods combined with physical background in wide field of view imaging atmospheric Cherenkov telescopes".NUCLEAR SCIENCE AND TECHNIQUES 35.4(2024). |
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