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Determination of responses of liquid xenon to low energy electron and nuclear recoils using a PandaX-Ⅱ detector

Chinese Physics C 2021年第7期45卷 273-282页

作者： Binbin Yan Abdusalam Abdukerim Wei Chen Xun Chen Yunhua Chen Chen Cheng Xiangyi Cui Yingjie Fan Deqing Fang Changbo Fu Mengting Fu Lisheng Geng Karl Giboni Linhui Gu Xuyuan Guo Ke Han Changda He Di Huang Peiyao Huang Yan Huang Yanlin Huang Zhou Huang Xiangdong Ji Yonglin Ju Shuaijie Li Jianglai Liu Zhuoqun Lei Wenbo Ma Yugang Ma Yajun Mao Yue Meng Kaixiang Ni Jinhua Ning Xuyang Ning Xiangxiang Ren Changsong Shang Lin Si Guofang Shen Andi Tan Anqing Wang Hongwei Wang Meng Wang Qiuhong Wang Siguang Wang Wei Wang Xiuli Wang Zhou Wang Mengmeng Wu Shiyong Wu Weihao Wu Jingkai Xia Mengjiao Xiao Pengwei Xie Rui Yan Jijun Yang Yong Yang Chunxu Yu Jumin Yuan Ying Yuan Jianfeng Yue Xinning Zeng Dan Zhang Tao Zhang Li Zhao Qibin Zheng Jifang Zhou Ning Zhou Xiaopeng Zhou School of Physics and Astronomy Shanghai Jiao Tong UniversityMOE Key Laboratory for Particle Astrophysics and CosmologyShanghai Key Laboratory for Particle Physics and CosmologyShanghai 200240China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China Key Laboratory of Nuclear Physics and Ion-beam Application(MOE) Institute of Modern PhysicsFudan UniversityShanghai 200433China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213China Yalong River Hydropower Development Company Ltd.288 Shuanglin RoadChengdu 610051China School of Physics Sun Yat-Sen UniversityGuangzhou 510275China Tsung-Dao Lee Institute Shanghai 200240China School of Physics Nankai UniversityTianjin 300071China School of Physics Peking UniversityBeijing 100871China School of Physics Beihang UniversityBeijing 100191China International Research Center for Nuclei and Particles in the Cosmos&Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang UniversityBeijing 100191China School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Physics University of MarylandCollege ParkMaryland 20742USA School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physics and Key Laboratory of Particle Physics and Particle Irradiation(MOE) Shandong UniversityJinan 250100China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China Center for High Energy Physics Peking UniversityBeijing 100871China

We present a systematic determination of the responses of PandaX-Ⅱ,a dual phase xenon time projection chamber detector,to low energy *** electron recoil(ER) and nuclear recoil(NR) responses are calibrated,respectively,with injected tritiated methane or 220Rn source,and with 241Am-Be neutron source,in an energy range from 1-25 keV(ER) and 4-80 keV(NR),under the two drift fields,400 and 317 V/*** empirical model is used to fit the light yield and charge yield for both types of *** best fit models can describe the calibration data *** systematic uncertainties of the fitted models are obtained via statistical comparison to the data.

关键词： dark matter liquid xenon time projection chamber calibration electron recoil nucleon recoil NEST2.0

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Signal response model in PandaX-4T

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Physical Review D 2024年第2期110卷 023029-023029页

作者： Yunyang Luo Zihao Bo Shibo Zhang Abdusalam Abdukerim Wei Chen Xun Chen Chen Cheng Zhaokan Cheng Xiangyi Cui Yingjie Fan Deqing Fang Lisheng Geng Karl Giboni Xuyuan Guo Chencheng Han Ke Han Changda He Jinrong He Di Huang Junting Huang Zhou Huang Ruquan Hou Yu Hou Xiangdong Ji Yonglin Ju Chenxiang Li Jiafu Li Mingchuan Li Shuaijie Li Tao Li Qing Lin Jianglai Liu Congcong Lu Xiaoying Lu Lingyin Luo Wenbo Ma Yugang Ma Yajun Mao Yue Meng Xuyang Ning Binyu Pang Ningchun Qi Zhicheng Qian Xiangxiang Ren Nasir Shaheed Xiaofeng Shang Xiyuan Shao Guofang Shen Lin Si Wenliang Sun Andi Tan Yi Tao Anqing Wang Meng Wang Qiuhong Wang Shaobo Wang Siguang Wang Wei Wang Xiuli Wang Xu Wang Zhou Wang Yuehuan Wei Mengmeng Wu Weihao Wu Yuan Wu Mengjiao Xiao Xiang Xiao Binbin Yan Xiyu Yan Yong Yang Chunxu Yu Ying Yuan Zhe Yuan Youhui Yun Xinning Zeng Minzhen Zhang Peng Zhang Shu Zhang Tao Zhang Wei Zhang Yang Zhang Yingxin Zhang Yuanyuan Zhang Li Zhao Jifang Zhou Ning Zhou Xiaopeng Zhou Yong Zhou Yubo Zhou Zhizhen Zhou Department of Modern Physics University of Science and Technology of China Hefei 230026 China School of Physics and Astronomy Shanghai Jiao Tong University Key Laboratory for Particle Astrophysics and Cosmology (MoE) Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai 200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China School of Physics Sun Yat-Sen University Guangzhou 510275 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China Department of Physics Yantai University Yantai 264005 China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China School of Physics Beihang University Beijing 102206 China International Research Center for Nuclei and Particles in the Cosmos and Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China School of Physics and Microelectronics Zhengzhou University Zhengzhou Henan 450001 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu 610051 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park Maryland 20742 USA State Key Laboratory of Particle Detection and Electronics New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Qingdao 266237 Shandong China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao 266237 Shandong China School of Physics Peking University Beijing 100871 China School of Physics Nankai University Tianjin 300071 China SJTU Paris Elite Insti

PandaX-4T experiment is a deep-underground dark matter direct search experiment that employs a dual-phase time projection chamber with a sensitive volume containing 3.7 tonne of liquid xenon. The detector of PandaX-4T is capable of simultaneously collecting the primary scintillation and ionization signals, utilizing their ratio to discriminate dark matter signals from background sources such as gamma rays and beta particles. The signal response model plays a crucial role in interpreting the data obtained by PandaX-4T. It describes the conversion from the deposited energy by dark matter interactions to the detectable signals within the detector. The signal response model is utilized in various PandaX-4T results. This work provides a comprehensive description of the procedures involved in constructing and parameter-fitting the signal response model for the energy range of approximately 1 keV to 25 keV for electronic recoils and 6 keV to 90 keV for nuclear recoils. It also covers the signal reconstruction, selection, and correction methods, which are crucial components integrated into the signal response model.

关键词： Particle dark matter Particle data analysis

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Cyclicalnet: Modeling Temporal Dependencies with 2d Shifts for Exploring Underlying Patterns in Sequential Vibration Signals

SSRN

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SSRN 2023年

作者： Xu, Chaojun Chen, Peng Li, Zheng Ma, Zhigang Zhou, Chengning College of Engineering Shantou University 243 Daxue Road Jingping District Guangdong Shantou515063 China Key Laboratory of Intelligent Manufacturing Technology Ministry of Education 243 Daxue Road Jingping District Guangdong Shantou515063 China Business School The University of Sydney Abercrombie Building H70 Corner Abercrombie Street and Codrington St Darlington SydneyNSW2006 Australia Nuclear Power Institute of China 328 Changshun Road Shuangliu District Chengdu610213 China

Planetary gearboxes are vital parts of wind turbines, but they are vulnerable to failure from problems such as complex vibrations caused by changing loads, harsh operating environments, contamination, and other factors. As a result, developing effective fault diagnosis methods for planetary gearboxes is crucial. Deep learning models, especially artificial general architectures like Transformers, ResNets, DenseNets, and GANs, have shown promise in capturing intricate patterns in time series data to achieve fault diagnosis tasks. However, one-dimensional convolution kernels have limited scope, as they can only capture patterns from neighboring time series points. Moreover, attention mechanisms-based methods also face difficulties in identifying consistent dependencies among sparse points hidden within complex patterns. To address these challenges, this paper proposes a novel architecture, Cyclicalnet, designed to capture the inherent cyclicity and complex temporal fluctuations within vibration signals, thus enhancing fault diagnosis and predictive maintenance strategies. The proposed architecture leverages cyclical components, embeddings, and a densely connected manner. First, it takes the top amplitude-frequency components from vibration signals as input to cyclical blocks, unlike traditional methods. Then, position and token embeddings are incorporated to capture and encode the sequential nature of the vibration signals. The position embeddings help comprehend the various locations and sequential structure, while the token embeddings focus on conveying the different components or tokens within the signal. By combining token and position embeddings, the embedding block generates a comprehensive representation of the sequence, benefiting downstream tasks relying on this input. Next, a reshape operation that facilitates subsequent 2D-Net feature extraction transforms the 1D sequential representation into 2D maps. Furthermore, dense connections between blocks explore un

关键词： Failure analysis

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Extraction of offshore navigation characteristics based on crossing-line analysis

Extraction of offshore navigation characteristics based on c...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Wanbing Song Yongjie Zhu Yi Zuo Tieshan Li C.L. Philip Chen Navigation College Dalian Maritime University Dalian China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728162461

ISBN: (纸本)9781665419352

The offshore channel is an important stage of preparation for the docking because it exists a higher frequency of ship traffic accidents. The analysis of channel is a critical premise for ship risk assessment and an indispensable part for improving the efficiency of shipping management. A channel analysis based on crossing-line has been widely used in inland channel research, but it is seldom in offshore one. In order to reasonably analyze the navigation characteristics of offshore ships and provide a basis for an effective simulation of offshore navigation traffic model, a modified crossing-line analysis for offshore channel based on Automatic identification system (AIS) data is proposed. Our method is applied to the offshore channel analysis of Dalian Port, and its results show that this method has smaller statistical error in the analysis of curved channel and sector channel, which is effective to analyze the navigation characteristics of offshore channel. The proposed method could be used to simulate the ship speed model on the offshore curved channel and establish an intelligent channel system to optimize the ship tracking path.

关键词： Analytical models Navigation Statistical analysis Data models Risk management Marine vehicles Artificial intelligence

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Predicting Ship Fuel Consumption based on LSTM Neural Network

Predicting Ship Fuel Consumption based on LSTM Neural Networ...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Yongjie Zhu Yi Zuo Tieshan Li Navigation College Dalian Maritime University Dalian China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728162461

ISBN: (纸本)9781665419352

With the rapid development of marine trade and transportation, a large number of ships sail at sea. The ship energy conservation and environment protection are becoming public interested problems. Precise prediction of ship fuel consumption is vital for controlling emissions from ships. Some scholars used regression analysis to interpret the relations between fuel consumption and its influence factors. However, considering that there are a large amount of influence factors affecting ship fuel consumption, such as pitch, ship speed, wind and wave, some machine learning models are employed. While, ship fuel consumption data is time-related. A time series problem arises. The regression analysis and machine learning models cannot capture the time series characteristics. Therefore, LSTM neural network is applied in this paper. Experimentally, we compared LSTM with three traditional machine learning models, namely linear regression (LR), support vector regression (SVR), and artificial neural network (ANN). It found that the prediction accuracy can be improved with 11.8% compared with artificial neural network. The LSTM model can catch the time series characteristics of fuel consumption data and get a higher accuracy.

关键词： Computational modeling Time series analysis Machine learning Predictive models Data models Fuels Marine vehicles

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Anisotropy and polarization of x-ray line emissions in the dielectronic recombination of hydrogenlike Fe25+ ions

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Physical Review A 2021年第2期104卷 022809-022809页

作者： Zhihao Yang Junwen Gao Wenmin Yan Ke Yao Jiamin Yang Zhongwen Wu Zhimin Hu Key Laboratory of Radiation Physics and Technology of Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu 610064 China Laser Fusion Research Center China Academy of Engineering Physics Mianyang 621900 China Institute of Atomic and Molecular Physics Jilin University Changchun 130012 China Shanghai EBIT Laboratory Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE) Fudan University Shanghai 200433 China Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province College of Physics and Electronic Engineering Northwest Normal University Lanzhou 730070 China

We present a systematic study of the anisotropy and polarization of x-ray line emissions in KLL dielectronic recombination of hydrogenlike Fe25+ ions. For the KLL dielectronic recombination considered, it consists of a resonant electron capture plus a two-step radiative decay. While the anisotropy and polarization of the first-step decay photons are calculated by using the flexible atomic code, those of the second-step decay photons are obtained with two methods, i.e., the formalism of the deorientation factor and density matrix theory. Resonance strengths are presented as well to synthesize the dielectronic satellite line spectra. The synthetic spectra including the correction of anisotropy and polarization effects show better agreement with existing experiments than previous calculations. This indicates that these effects are important for the intensity distribution of the dielectronic satellite lines.

关键词： Electronic excitation & ionization

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An Intelligent Wireless Displacement Sensor for Landslide Monitoring and Early Warning

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IOP Conference Series: Earth and Environmental Science 2021年第7期861卷

作者： Xing Zhu Hewei Xi Zhaoqing He Liu Yang State Key Laboratory Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu 610059 China College of Computer Science and Cyber Security Chengdu Univeristy of Technology Chengdu 610059 China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu 610059 China

Landslide poses a great threat to the safety of local communities, engineering construction and operation. Accurate monitoring and successful early-warning of rapid landslides using conventional techniques with constant and sparse sampling strategy remains a challenge in geo-disaster research and mitigation. Sampling and uploading all sensor data to cloud imposes huge burden on the power supply and radio bandwidth, which greatly reduces the long-term stability and robustness of the wireless sensor in field. With aim to achieve the best powerful sensing ability based on limited power and bandwidth of sensor, we proposed a low-cost and energy efficient wireless crack-meter sensor integrated with intelligent computing architecture for rapid deformation monitoring. A self-adaptive data sampling and uploading algorithm based on the variation of objective physical value was implemented in the embedded microcontroller of sensor node, and only the valuable information derived from sensor data to the cloud server. Experimental results demonstrated that the intelligent sensing approach can effectively collect the crucial data at a high frequency during accelerating deformation of landslide, while only update status at several hours during non-deformation phase. Several landslide monitoring applications have proved that this intelligent sensing can significantly and effectively enhance the success rate and real-time performance of early-warning for rapid landslide.

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Search for Light Fermionic Dark Matter Absorption on Electrons in PandaX-4T

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Physical Review Letters 2022年第16期129卷 161804-161804页

作者： Dan Zhang Abdusalam Abdukerim Zihao Bo Wei Chen Xun Chen Yunhua Chen Chen Cheng Zhaokan Cheng Xiangyi Cui Yingjie Fan Deqing Fang Changbo Fu Mengting Fu Lisheng Geng Karl Giboni Linhui Gu Xuyuan Guo Ke Han Changda He Jinrong He Di Huang Yanlin Huang Zhou Huang Ruquan Hou Xiangdong Ji Yonglin Ju Chenxiang Li Jiafu Li Mingchuan Li Shu Li Shuaijie Li Qing Lin Jianglai Liu Xiaoying Lu Lingyin Luo Yunyang Luo Wenbo Ma Yugang Ma Yajun Mao Nasir Shaheed Yue Meng Xuyang Ning Ningchun Qi Zhicheng Qian Xiangxiang Ren Changsong Shang Xiaofeng Shang Guofang Shen Lin Si Wenliang Sun Andi Tan Yi Tao Anqing Wang Meng Wang Qiuhong Wang Shaobo Wang Siguang Wang Wei Wang Xiuli Wang Zhou Wang Yuehuan Wei Mengmeng Wu Weihao Wu Jingkai Xia Mengjiao Xiao Xiang Xiao Pengwei Xie Binbin Yan Xiyu Yan Jijun Yang Yong Yang Chunxu Yu Jumin Yuan Ying Yuan Xinning Zeng Minzhen Zhang Peng Zhang Shibo Zhang Shu Zhang Tao Zhang Yuanyuan Zhang Li Zhao Qibin Zheng Jifang Zhou Ning Zhou Xiaopeng Zhou Yong Zhou Yubo Zhou Shao-Feng Ge Xiao-Gang He Xiao-Dong Ma Jie Sheng Department of Physics University of Maryland College Park Maryland 20742 USA School of Physics and Astronomy Shanghai Jiao Tong University Key Laboratory for Particle Astrophysics and Cosmology (MoE) Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai 200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu 610051 China School of Physics Sun Yat-Sen University Guangzhou 510275 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China School of Physics Nankai University Tianjin 300071 China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China School of Physics Peking University Beijing 100871 China School of Physics Beihang University Beijing 102206 China International Research Center for Nuclei and Particles in the Cosmos and Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China School of Physics and Microelectronics Zhengzhou University Zhengzhou Henan 450001 China School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology Shanghai 200093 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei 230026 China Department of Modern Physics University of Science and Technology of China Hefei 230026 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Scienc Shandong University Qingdao 266237 Shandong China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao 266237 Shandong China SJTU Par

We report a search on sub-MeV fermionic dark matter absorbed by electrons with an outgoing active neutrino using the 0.63 tonne year exposure collected by the PandaX-4T liquid xenon experiment. No significant signals are observed over the expected background. The data are interpreted into limits to the effective couplings between such dark matter and the electron. For axial-vector or vector interactions, our sensitivity is competitive in comparison to existing astrophysical bounds on the decay of such a dark matter candidate into photon final states. In particular, we present the first direct detection limits for a vector (axial-vector) interaction which are the strongest in the mass range from 35 to 55 (25 to 45) keV/c2 in comparison to other astrophysical and cosmological constraints.

关键词： Effective field theory Fermions Particle dark matter Dark matter detectors Multi-purpose particle detectors

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First Search for the Absorption of Fermionic Dark Matter with the PandaX-4T Experiment

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Physical Review Letters 2022年第16期129卷 161803-161803页

作者： Linhui Gu Abdusalam Abdukerim Zihao Bo Wei Chen Xun Chen Yunhua Chen Chen Cheng Yunshan Cheng Zhaokan Cheng Xiangyi Cui Yingjie Fan Deqing Fang Changbo Fu Mengting Fu Lisheng Geng Karl Giboni Xuyuan Guo Ke Han Changda He Jinrong He Di Huang Yanlin Huang Zhou Huang Ruquan Hou Xiangdong Ji Yonglin Ju Chenxiang Li Jiafu Li Mingchuan Li Shu Li Shuaijie Li Qing Lin Jianglai Liu Xiaoying Lu Lingyin Luo Yunyang Luo Wenbo Ma Yugang Ma Yajun Mao Nasir Shaheed Yue Meng Xuyang Ning Ningchun Qi Zhicheng Qian Xiangxiang Ren Changsong Shang Xiaofeng Shang Guofang Shen Lin Si Wenliang Sun Andi Tan Yi Tao Anqing Wang Meng Wang Qiuhong Wang Shaobo Wang Siguang Wang Wei Wang Xiuli Wang Zhou Wang Yuehuan Wei Mengmeng Wu Weihao Wu Jingkai Xia Mengjiao Xiao Xiang Xiao Pengwei Xie Binbin Yan Xiyu Yan Jijun Yang Yong Yang Yukun Yao Chunxu Yu Jumin Yuan Ying Yuan Xinning Zeng Dan Zhang Minzhen Zhang Peng Zhang Shibo Zhang Shu Zhang Tao Zhang Yuanyuan Zhang Li Zhao Qibin Zheng Jifang Zhou Ning Zhou Xiaopeng Zhou Yong Zhou Yubo Zhou School of Physics and Astronomy Shanghai Jiao Tong University Key Laboratory for Particle Astrophysics and Cosmology (MoE) Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai 200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu 610051 China School of Physics Sun Yat-Sen University Guangzhou 510275 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Qingdao 266237 Shandong China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao 266237 Shandong China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China School of Physics Nankai University Tianjin 300071 China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China School of Physics Peking University Beijing 100871 China School of Physics Beihang University Beijing 102206 China International Research Center for Nuclei and Particles in the Cosmos and Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China School of Physics and Microelectronics Zhengzhou University Zhengzhou Henan 450001 China School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology Shanghai 200093 China Department of Physics University of Maryland College Park Maryland 20742 USA School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei 230026 China Department of Modern Physics University of Science and Technology of China Hefei 230026 China SJTU Pa

Compared with the signature of dark matter elastic scattering off nuclei, the absorption of fermionic dark matter by nuclei opens up a new searching channel for light dark matter with a characteristic monoenergetic signal. In this Letter, we explore the 95.0-day data from the PandaX-4T commissioning run and report the first dedicated searching results of the fermionic dark matter absorption signal through a neutral current process. No significant signal was found, and the lowest limit on the dark matter-nucleon interaction cross section is set to be 1.5×10−50 cm2 for a fermionic dark matter mass of 40 MeV/c2 with 90% confidence level.

关键词： Particle dark matter Dark matter detectors

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Direct Measurement of the Astrophysical F19(p,αγ)O16 Reaction in the Deepest Operational Underground Laboratory

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Physical Review Letters 2021年第15期127卷 152702-152702页

作者： L. Y. Zhang J. Su J. J. He M. Wiescher R. J. deBoer D. Kahl Y. J. Chen X. Y. Li J. G. Wang L. Zhang F. Q. Cao H. Zhang Z. C. Zhang T. Y. Jiao Y. D. Sheng L. H. Wang L. Y. Song X. Z. Jiang Z. M. Li E. T. Li S. Wang G. Lian Z. H. Li X. D. Tang H. W. Zhao L. T. Sun Q. Wu J. Q. Li B. Q. Cui L. H. Chen R. G. Ma B. Guo S. W. Xu J. Y. Li N. C. Qi W. L. Sun X. Y. Guo P. Zhang Y. H. Chen Y. Zhou J. F. Zhou J. R. He C. S. Shang M. C. Li X. H. Zhou Y. H. Zhang F. S. Zhang Z. G. Hu H. S. Xu J. P. Chen W. P. Liu Key Laboratory of Beam Technology and Material Modification of Ministry of Education College of Nuclear Science and Technology Beijing Normal University Beijing 100875 China Department of Physics and The Joint Institute for Nuclear Astrophysics University of Notre Dame Notre Dame Indiana 46556 USA Extreme Light Infrastructure—Nuclear Physics Horia Hulubei National Institute for Research and Development in Physics and Nuclear Engineering (IFIN-HH) Bucharest-Măgurele 077125 Romania Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China China Institute of Atomic Energy Beijing 102413 China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China Shandong Provincial Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment Institute of Space Sciences Shandong University Weihai 264209 China Yalong River Hydropower Development Company Chengdu 610051 China

Fluorine is one of the most interesting elements in nuclear astrophysics, where the F19(p,α)O16 reaction is of crucial importance for Galactic F19 abundances and CNO cycle loss in first generation Population III stars. As a day-one campaign at the Jinping Underground nuclear Astrophysics experimental facility, we report direct measurements of the essential F19(p,αγ)O16 reaction channel. The γ-ray yields were measured over Ec.m.=72.4–344 keV, covering the Gamow window; our energy of 72.4 keV is unprecedentedly low, reported here for the first time. The experiment was performed under the extremely low cosmic-ray-induced background environment of the China JinPing Underground Laboratory, one of the deepest underground laboratories in the world. The present low-energy S factors deviate significantly from previous theoretical predictions, and the uncertainties are significantly reduced. The thermonuclear F19(p,αγ)O16 reaction rate has been determined directly at the relevant astrophysical energies.

关键词： H & He burning H & He induced nuclear reactions nuclear astrophysics Resonance reactions

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