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38th International Cosmic Ray Conference, ICRC 2023

作者： Cui, Ming-Yang Xia, Jie He, Jia-Yin Shi, Guang-Lu Feng, Li Yuan, Qiang Zhang, Yi Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China School of Astronomy and Space Science University of Science and Technology of China Hefei230026 China Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Physics and Astronomy Sun Yat-sen University Zhuhai519000 China School of Physics Guangdong Guangzhou510275 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen Unive

During the propagation of cosmic rays (CRs) in the solar system, they are blocked by the Sun and deflected by the magnetic field, resulting in a shadow on the celestial map. As CRs travel from the vicinity of the Sun to the Earth, they encounter deflection from the coronal magnetic field (CMF), the interplanetary magnetic field (IMF), and finally the geomagnetic field (GMF). The extent of deflection is determined by the magnetic field’s intensity, direction, and energy of the CRs. Variations in the magnetic field cause corresponding changes in the position and size of the Sun’s shadow. By observing the Sun’s shadow of CRs, we can investigate the magnetic fields between the Sun and the Earth, complementing other measurements on these magnetic fields. Furthermore, it serves as a means to validate magnetic field models. The Large High Altitude Air Shower Observatory (LHAASO) is a new generation cosmic-ray and gamma-ray experiment in Daocheng, western China. With a vast Water Cherenkov Detector Array (WCDA) spanning 78,000 m2, LHAASO can successfully observe the significant Sun’s shadow within just a few days. Our study uses the WCDA to measure the Sun’s shadow effect caused by CRs over multiple Carrington rotation periods. Subsequently, we compare these results with simulations based on different magnetic field models. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron

arXiv

引用

arXiv 2022年

作者： Liu, Wanli Li, Chen Xu, Ning Jiang, Tao Rahaman, Md Mamunur Sun, Hongzan Wu, Xiangchen Hu, Weiming Chen, Haoyuan Sun, Changhao Yao, Yudong Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Liaoning Petrochemical University Fushun China School of Control Engineering Chengdu University of Information Technology Chengdu China Shengjing Hospital China Medical University Shenyang China Suzhou Ruiqian Technology Company Ltd. Suzhou China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken United States Institute of Medical Informatics University of Luebeck Luebeck Germany

Cervical cancer is the seventh most common cancer among all the cancers worldwide and the fourth most common cancer among women. Cervical cytopathology image classification is an important method to diagnose cervical cancer. Manual screening of cytopathology images is time-consuming and error-prone. The emergence of the automatic computer-aided diagnosis system solves this problem. This paper proposes a framework called CVM-Cervix based on deep learning to perform cervical cell classification tasks. It can analyze pap slides quickly and accurately. CVM-Cervix first proposes a Convolutional Neural Network module and a Visual Transformer module for local and global feature extraction respectively, then a Multilayer Perceptron module is designed to fuse the local and global features for the final classification. Experimental results show the effectiveness and potential of the proposed CVM-Cervix in the field of cervical Pap smear image classification. In addition, according to the practical needs of clinical work, we perform a lightweight post-processing to compress the model. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

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Search for Cosmic-Ray Boosted Sub-MeV Dark-Matter–Electron Scattering in PandaX-4T

引用

Physical Review Letters 2024年第10期133卷 101805页

作者： Xiaofeng Shang Abdusalam Abdukerim Zihao Bo Wei Chen Xun Chen Yunhua 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 Xiangpan Ji Yonglin Ju Chenxiang Li Jiafu Li Mingchuan Li Shuaijie Li Tao Li Qing Lin Jianglai Liu Congcong Lu Xiaoying Lu Lingyin Luo Yunyang Luo Wenbo Ma Yugang Ma Yajun Mao Yue Meng Xuyang Ning Binyu Pang Ningchun Qi Zhicheng Qian Xiangxiang Ren Nasir Shaheed Xiyuan Shao Guofang Shen Manbin Shen Lin Si Wenliang Sun 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 Kaizhi Xiong Binbin Yan Xiyu Yan Yong Yang Chunxu Yu Ying Yuan Zhe Yuan Youhui Yun Xinning Zeng Minzhen Zhang Peng Zhang Shibo Zhang Shu Zhang Tao Zhang Wei Zhang Yang Zhang Yingxin Zhang Yuanyuan Zhang Li Zhao Jifang Zhou Ning Zhou Xiaopeng Zhou Yubo Zhou Zhizhen Zhou Shao-Feng Ge Chen Xia School of Physics and Astronomy New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province Liangshan 615000 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 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 Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing 100191 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 Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 China School of Mechanical Engineering Department of Physics University of Maryland College Park Maryland 20742 USA School of Physics Nankai University Tianjin 300071 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 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 SJTU Paris Elite Institute of Technology School of Physic

We report the first search for the elastic scatterings between cosmic-ray boosted sub-MeV dark matter (DM) and electrons in the PandaX-4T liquid xenon experiment. Sub-MeV DM particles can be accelerated by scattering with electrons in the cosmic rays and produce detectable electron recoil signals in the detector. Using the commissioning data from PandaX-4T of 0.63 tonne·year exposure, we set new constraints on DM-electron scattering cross sections for DM masses ranging from 10 eV/c2 to 3 keV/c2.

关键词： Dark matter Particle dark matter Dark matter detectors

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Evaluation model construction of automobile appearance design based on random forest algorithm

引用

Journal of Physics: Conference Series 2021年第1期1941卷

作者： Xundong Zheng Shanshan Yi Xiaotong Deng College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

Random forest algorithm is a nonlinear supervised classification algorithm, which is based on the idea of ensemble learning and consists of multiple decision tree classifiers. The algorithm has strong generalization ability, fault tolerance ability and nonlinear fitting ability, and can learn the deep-seated laws in the sample system according to a small number of samples using information entropy as the criterion. In the form of questionnaire survey, this paper collects the main evaluation factors of automobile appearance modeling design for 100 consumers or potential consumers. After selecting the evaluation factors, 50 consumers or potential consumers score a car according to these evaluation factors, to collect the original data. Experimental results show that the accuracy of the algorithm is as high as 84%, which shows that the model has such a high accuracy that it can be applied to the evaluation of automobile appearance modeling design. At the same time, this method can also be transferred to the appearance modeling design of other models.

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Look-ahead natural evolutionary strategies 20

Look-ahead natural evolutionary strategies

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2020 Genetic and Evolutionary Computation Conference, GECCO 2020

作者： Jia, Jin Liang Saldivar, Alfredo Alan Flores Li, Lin Li, Yun School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China Industry 4.0 Artificial Intelligence Laboratory Dongguan University of Technology Dongguan523808 China College of Science and Information Qingdao Agricultural University Qingdao266109 China

ISBN: (纸本)9781450371278

Natural1 evolution strategy (NES) algorithms have seen a variety of successful applications recently, but still need to improve algorithmic convergence. To address this issue, we propose the use of a look-ahead scheme predictively to estimate the natural acceleration in the algorithm. This estimation is to assist convergence towards the global optimum faster. The resultant algorithm is termed a "Look-Ahead Natural Evolutionary Strategy" (LANES) algorithm. It is tested against a combination of multiple benchmarks using a number of well-known benchmark problems. Compared with the original NES, the LANES increases the optimizer overheads, but offers a steady improvement in optimality, accuracy, and convergence. © 2020 Owner/Author.

关键词： Machine learning

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Progressive contrastive representation learning for defect diagnosis in aluminum disk substrates with a bio-inspired vision sensor

引用

Expert Systems with Applications 2025年 289卷

作者： Peng Chen Ruijin Zhang Changbo He Yaqiang Jin Shuai Fan Junyu Qi Chengning Zhou Chun Zhang College of Engineering Shantou University Shantou 515063 Guangdong China Key Laboratory of Intelligent Manufacturing Technology Ministry of Education Shantou 515063 Guangdong China College of Electrical Engineering and Automation Anhui University Hefei 230601 China School of Qilu Transportation Shandong University Jinan 250061 Shandong China School of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu 610059 Sichuan China Electronics & Drives Reutlingen University Reutlingen 72762 Germany Nuclear Power Institute of China Chengdu 610213 Sichuan China

Traditional industrial surface defect detection using CCD/CMOS cameras faces limitations in detecting minute defects on aluminum substrates in dynamic industrial scenes. While event cameras can capture small defects through event information flow (EIF), they struggle with noise-related challenges. Additionally, current contrastive learning frameworks employ simplified loss computation mechanisms that limit their capability to discover and characterize novel categories. This study addresses these challenges through two main innovations: (1) a novel event stream imaging technique that combines equal time window segmentation, overlapping windows, and Gaussian filtering to enhance data quality, and (2) Progressive Contrastive Representation Learning (PCRL), a sophisticated framework for handling both known and unknown fault classes. The PCRL framework overcomes traditional limitations through a four-stage loss function strategy and structured analysis of intra-cluster and inter-cluster relationships, enabling better feature extraction and model convergence for previously unseen patterns. Through systematic validation, our approach demonstrates superior performance in noise reduction, unknown fault detection, and precise surface defect classification, offering a robust solution for industrial applications. Through systematic validation, our methodology exhibits exceptional efficacy in mitigating signal noise, identifying novel fault patterns, and executing high-precision surface anomaly categorization, yielding a comprehensive accuracy rate of 93.51 % across all test cases and demonstrating remarkable performance with 95.36 % accuracy in previously unencountered fault categories, thereby presenting a sophisticated solution for industrial implementations while establishing a foundation for subsequent scholarly investigations in this field.

关键词： Aluminum disk substrates Bio-inspired vision sensor Contrastive learning Event stream imaging Fault diagnosis Multi-stage loss function Semi-supervised learning

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Search for Solar Boosted Dark Matter Particles at the PandaX-4T Experiment

arXiv

引用

arXiv 2024年

作者： Shen, Guofang Bo, Zihao Chen, Wei Chen, Xun Chen, Yunhua Cheng, Zhaokan Cui, Xiangyi Fan, Yingjie Fang, Deqing Gao, Zhixing Geng, Lisheng Giboni, Karl Guo, Xunan Guo, Xuyuan Guo, Zichao Han, Chencheng Han, Ke He, Changda He, Jinrong Huang, Di Huang, Houqi Huang, Junting Hou, Ruquan Hou, Yu Ji, Xiangdong Ji, Xiangpan Ju, Yonglin Li, Chenxiang Li, Jiafu Li, Mingchuan Li, Shuaijie Li, Tao Li, Zhiyuan Lin, Qing Liu, Jianglai Lu, Congcong Lu, Xiaoying Luo, Lingyin Luo, Yunyang Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Ning, Xuyang Pang, Binyu Qi, Ningchun Qian, Zhicheng Ren, Xiangxiang Shan, Dong Shang, Xiaofeng Shao, Xiyuan Shen, Manbin Sun, Wenliang Tao, Yi Wang, Anqing Wang, Guanbo Wang, Hao Wang, Jiamin Wang, Lei Wang, Meng Wang, Qiuhong Wang, Shaobo Wang, Siguang Wang, Wei Wang, Xiuli Wang, Xu Wang, Zhou Wei, Yuehuan Wu, Weihao Wu, Yuan Xiao, Mengjiao Xiao, Xiang Xiong, Kaizhi Xu, Yifan Yao, Shunyu Yan, Binbin Yan, Xiyu Yang, Yong Ye, Peihua Yu, Chunxu Yuan, Ying Yuan, Zhe Yun, Youhui Zeng, Xinning Zhang, Minzhen Zhang, Peng Zhang, Shibo Zhang, Shu Zhang, Tao Zhang, Wei Zhang, Yang Zhang, Yingxin Zhang, Yuanyuan Zhao, Li Zhou, Jifang Zhou, Jiaxu Zhou, Jiayi Zhou, Ning Zhou, Xiaopeng Zhou, Yubo Zhou, Zhizhen An, Haipeng Nie, Haoming New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai201210 China School of Physics Beihang University Beijing102206 China Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu610051 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai519082 China Department of Physics Yantai University Yantai264005 China Institute of Modern Physics Fudan University Shanghai200433 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing100191 China International Research Center for Nuclei and Particles in the Cosmos Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing100191 China Institute of Modern Physics Chinese Academy of Sciences Huizhou516000 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Physics University of Maryland College ParkMD20742 United States School of Physics Nankai University Tianjin300071 China School of Physics Sun Yat-Sen University Guangzhou510275 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei230026 China Department of Modern Physics University of Science and Technology of China Hefei230026 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Shandong Qingdao266237

We present a novel constraint on light dark matter utilizing 1.54 tonne·year of data acquired from the PandaX-4T dual-phase xenon time projection chamber. This constraint is derived through detecting electronic recoil signals resulting from the interaction with solar-enhanced dark matter flux. Low-mass dark matter particles, lighter than a few MeV/c2, can scatter with the thermal electrons in the Sun. Consequently, with higher kinetic energy, the boosted dark matter component becomes detectable via contact scattering with xenon electrons, resulting in a few keV energy deposition that exceeds the threshold of PandaX-4T. We calculate the expected recoil energy in PandaX-4T considering the Sun’s acceleration and the detection capabilities of the xenon detector. The first experimental search results using the xenon detector yield the most stringent cross-section of 3.51 × 10−39 cm2 at 0.08 MeV/c2 for a solar boosted dark matter mass ranging from 0.02 to 10 MeV/c2, achieving a 23 fold improvement compared with earlier experimental studies. © 2024, CC BY.

关键词： Kinetic energy

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Simulation study of turbulent mixing characteristics of tight lattice with triangular arrangement under blockage condition

引用

Journal of Physics: Conference Series 2021年第1期1985卷

作者： Yunxiang Li Dongmei Luo Xinyu Yang Chuyun Zhang College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu 610000 China

The study of turbulent mixing in the gaps of fluid sub-channels in the tight lattice under blockage conditions is of great significance to the prediction of the thermal-hydraulic behavior of fuel assemblies under accident conditions. The CFD method was used to simulate the fluid flow phenomenon in the blockage condition in the tight lattice, and the velocity field and the turbulent mixing coefficient distribution in the section and downstream of the blockage under different blockage conditions were further compared and analyzed. The analysis results show that the blockage mainly affects the flow field of the sub-channel where it is located and the adjacent sub-channels. The turbulent mixing coefficient in these channels will increase significantly; when the sub-channel blockage rate is small, the turbulent mixing coefficient downstream of the blockage will be reduced; the short length will make the flow field near the top of blockage segment more complicated. The obtained changes of transverse and axial turbulent mixing coefficients under different blockage conditions can provide a reference for the parameter setting of the sub-channel analysis program.

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Search for lepton portal dark matter in the PandaX-4T experiment

arXiv

引用

arXiv 2024年

作者： Ning, Xuyang Bo, Zihao Chen, Wei Chen, Xun Chen, Yunhua Cheng, Zhaokan Cui, Xiangyi Fan, Yingjie Fang, Deqing Gao, Zhixing Geng, Lisheng Giboni, Karl Guo, Xunan Guo, Xuyuan Guo, Zichao Han, Chencheng Han, Ke He, Changda He, Jinrong Huang, Di Huang, Houqi Huang, Junting Hou, Ruquan Hou, Yu Ji, Xiangdong Ji, Xiangpan Ju, Yonglin Li, Chenxiang Li, Jiafu Li, Mingchuan Li, Shuaijie Li, Tao Li, Zhiyuan Lin, Qing Liu, Jianglai Lu, Congcong Lu, Xiaoying Luo, Lingyin Luo, Yunyang Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Pang, Binyu Qi, Ningchun Qian, Zhicheng Ren, Xiangxiang Shan, Dong Shang, Xiaofeng Shao, Xiyuan Shen, Guofang Shen, Manbin Sun, Wenliang Tao, Yi Wang, Anqing Wang, Guanbo Wang, Hao Wang, Jiamin Wang, Lei Wang, Meng Wang, Qiuhong Wang, Shaobo Wang, Siguang Wang, Wei Wang, Xiuli Wang, Xu Wang, Zhou Wei, Yuehuan Wu, Weihao Wu, Yuan Xiao, Mengjiao Xiao, Xiang Xiong, Kaizhi Xu, Yifan Yao, Shunyu Yan, Binbin Yan, Xiyu Yang, Yong Ye, Peihua Yu, Chunxu Yuan, Ying Yuan, Zhe Yun, Youhui Zeng, Xinning Zhang, Minzhen Zhang, Peng Zhang, Shibo Zhang, Shu Zhang, Tao Zhang, Wei Zhang, Yang Zhang, Yingxin Zhang, Yuanyuan Zhao, Li Zhou, Jifang Zhou, Jiaxu Zhou, Jiayi Zhou, Ning Zhou, Xiaopeng Zhou, Yubo Zhou, Zhizhen Bai, Yang New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai201210 China Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province Liangshan615000 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu610051 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai519082 China Department of Physics Yantai University Yantai264005 China Institute of Modern Physics Fudan University Shanghai200433 China School of Physics Beihang University Beijing102206 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing100191 China International Research Center for Nuclei and Particles in the Cosmos Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing100191 China Institute of Modern Physics Chinese Academy of Sciences Huizhou516000 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Physics University of Maryland College ParkMD20742 United States School of Physics Nankai University Tianjin300071 China School of Physics Sun Yat-Sen University Guangzhou510275 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei230026 China Department of Modern Physics University of Science and Technology of China Hefei230026 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Qingdao266237 China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao266237 C

We report a search for a lepton-portal dark matter model, where dark matter couples to a charged lepton in the standard model. This simplified model naturally leads to photon-mediated dark matter interactions with nuclei, making it suitable for direct dark matter detection experiments. Matching to the framework of non-relativistic effective field theory for dark matter, we report the first sensitive search for this model using data from the PandaX-4T commissioning run. Our results yield strong constraints on Dirac fermion dark matter but relatively weaker constraints on Majorana dark matter due to the suppression of effective photon interactions. These constraints complement those obtained from the collider and indirect detection experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Photons

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Projected WIMP sensitivity of the CDEX-50 dark matter experiment

arXiv

引用

arXiv 2023年

作者： Geng, X.P. Yang, L.T. Yue, Q. Kang, K.J. Li, Y.J. An, H.P. Greeshma, C. Chang, J.P. Chen, Y.H. Cheng, J.P. Dai, W.H. Deng, Z. Fang, C.H. Gong, H. Guo, Q.J. Guo, T. Guo, X.Y. He, L. He, S.M. Hu, J.W. Huang, H.X. Huang, T.C. Jiang, L. Karmakar, S. Li, H.B. Li, H.Y. Li, J.M. Li, J. Li, Q.Y. Li, R.M.J. Li, X.Q. Li, Y.L. Liang, Y.F. Liao, B. Lin, F.K. Lin, S.T. Liu, J.X. Liu, S.K. Liu, Y.D. Liu, Y. Liu, Y.Y. Ma, H. Mao, Y.C. Nie, Q.Y. Ning, J.H. Pan, H. Qi, N.C. Ren, J. Ruan, X.C. Singh, M.K. Sun, T.X. Tang, C.J. Tian, Y. Wang, G.F. Wang, J.Z. Wang, L. Wang, Q. Wang, Y.F. Wang, Y.X. Wong, H.T. Wu, S.Y. Wu, Y.C. Xing, H.Y. Xu, R. Xu, Y. Xue, T. Yan, Y.L. Yi, N. Yu, C.X. Yu, H.J. Yue, J.F. Zeng, M. Zeng, Z. Zhang, B.T. Zhang, F.S. Zhang, L. Zhang, Z.H. Zhang, Z.Y. Zhao, J.Z. Zhao, K.K. Zhao, M.G. Zhou, J.F. Zhou, Z.Y. Zhu, J.J. Department of Engineering Physics Tsinghua University Beijing100084 China Department of Physics Tsinghua University Beijing100084 China Institute of Physics Academia Sinica Taipei11529 Taiwan NUCTECH Company Beijing100084 China YaLong River Hydropower Development Company Chengdu610051 China College of Nuclear Science and Technology Beijing Normal University Beijing100875 China College of Physics Sichuan University Chengdu610065 China School of Physics Peking University Beijing100871 China Department of Nuclear Physics China Institute of Atomic Energy Beijing102413 China Sino-French Institute of Nuclear and Technology Sun Yat-sen University Zhuhai519082 China School of Physics Nankai University Tianjin300071 China Department of Physics Banaras Hindu University Varanasi221005 India Department of Physics Beijing Normal University Beijing100875 China

CDEX-50 is a next-generation project of the China Dark Matter Experiment (CDEX) that aims to search for dark matter using a 50-kg germanium detector array. This paper comprises a thorough summary of the CDEX-50 dark matter experiment, including an investigation of potential background sources and the development of a background model. Based on the baseline model, the projected sensitivity of weakly interacting massive particle (WIMP) is also presented. The expected background level within the energy region of interest, set to 2–2.5 keVee, is ∼0.01 counts keVee−1 kg−1 day−1. At 90% confidence level, the expected sensitivity to spin-independent WIMP-nucleon couplings is estimated to reach a cross-section of 5.1 × 10−45 cm2 for a WIMP mass of 5 GeV/c2 with an exposure objective of 150 kg·year and an analysis threshold of 160 eVee. This science goal will correspond to the most sensitive results for WIMPs with a mass of 2.2–8 GeV/c2 Copyright © 2023, The Authors. All rights reserved.

关键词： Dark Matter

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