Calibration strategy of the JUNO-TAO experiment
文献类型:期刊论文
作者 | JUNO Collaboration; Zhan L(占亮)![]() |
刊名 | EUROPEAN PHYSICAL JOURNAL C
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出版日期 | 2022 |
卷号 | 82期号:12页码:1112 |
关键词 | NEUTRINO |
ISSN号 | 1434-6044 |
DOI | 10.1140/epjc/s10052-022-11069-3 |
文献子类 | Article |
英文摘要 | The Taishan Antineutrino Observatory (TAO or JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). Located near a reactor of the Taishan Nuclear Power Plant, TAO will measure the reactor antineutrino energy spectrum with an unprecedented energy resolution of < 2% at 1 MeV. Energy calibration is critical to achieve such a high energy resolution. Using the Automated Calibration Unit (ACU) and the Cable Loop System (CLS), multiple radioactive sources are deployed to various positions in the TAO detector for energy calibration. The residual non-uniformity can be controlled within 0.2%. The energy resolution degradation and energy bias caused by the residual non-uniformity can be controlled within 0.05% and 0.3%, respectively. The uncertainty of the non-linear energy response can be controlled within 0.6% with the radioactive sources of various energies, and could be further improved with cosmogenic 12B which is produced by the interaction of cosmic muon in the liquid scintillator. The stability of other detector parameters, e.g., the gain of each Silicon Photo multiplier, will be monitored with an ultraviolet LED calibration system. |
电子版国际标准刊号 | 1434-6052 |
语种 | 英语 |
WOS记录号 | WOS:000896642000003 |
源URL | [http://ir.ihep.ac.cn/handle/311005/297480] ![]() |
专题 | 江门中微子实验 高能物理研究所_实验物理中心 |
作者单位 | 中国科学院高能物理研究所 |
推荐引用方式 GB/T 7714 | JUNO Collaboration,Zhan L. Calibration strategy of the JUNO-TAO experiment[J]. EUROPEAN PHYSICAL JOURNAL C,2022,82(12):1112. |
APA | JUNO Collaboration,&Zhan L.(2022).Calibration strategy of the JUNO-TAO experiment.EUROPEAN PHYSICAL JOURNAL C,82(12),1112. |
MLA | JUNO Collaboration,et al."Calibration strategy of the JUNO-TAO experiment".EUROPEAN PHYSICAL JOURNAL C 82.12(2022):1112. |
入库方式: OAI收割
来源:高能物理研究所
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