检索结果-内蒙古大学图书馆

arXiv 2024年

作者： 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 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. 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. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Q.W. Tang, Z.B. Tian, W.W. Wang, C. Wang, C.B. Key Laboratory of Particle Astrophysics 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 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics 69029 Heidelberg103980 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 Sun Yat-Sen University Guangdong Guangzhou510275 China Sun Yat-Sen University Guangdong Zhuhai519000 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China APC Université Paris Cité CNRS IN2P3 CEA/IRFU Observatoire de Paris Paris119 75205 France Department of Engineering Physics Tsinghua Univers

KM2A is one of the main sub-arrays of LHAASO, working on gamma ray astronomy and cosmic ray physics at energies above 10 TeV. Detector simulation is the important foundation for estimating detector performance and data analysis. It is a big challenge to simulate the KM2A detector in the framework of Geant4 due to the need to track numerous photons from a large number of detector units (>6000) with large altitude difference (30 m) and huge coverage (1.3 km2). In this paper, the design of the KM2A simulation code G4KM2A based on Geant4 is introduced. The process of G4KM2A is optimized mainly in memory consumption to avoid memory overflow. Some simplifications are used to significantly speed up the execution of G4KM2A. The running time is reduced by at least 30 times compared to full detector simulation. The particle distributions and the core/angle resolution comparison between simulation and experimental data of the full KM2A array are also presented, which show good agreement. © 2024, CC BY-NC-ND.

关键词： Cosmic rays

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Adversarial directed graph embedding

arXiv

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arXiv 2020年

作者： Zhu, Shijie Li, Jianxin Peng, Hao Wang, Senzhang He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is to use adversarial mechanisms to deploy a discriminator and two generators that jointly learn each node's source and target vectors. For a given node, the two generators are trained to generate its fake target and source neighbor nodes from the same underlying distribution, and the discriminator aims to distinguish whether a neighbor node is real or fake. The two generators are formulated into a unified framework and could mutually reinforce each other to learn more robust source and target vectors. Extensive experiments show that DGGAN consistently and significantly outperforms existing state-of-the-art methods across multiple graph mining tasks on directed graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

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Observing Geometry of Quantum states in a Three-Level System

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Physical Review Letters 2020年第15期125卷 150401-150401页

作者： Jie Xie Aonan Zhang Ningping Cao Huichao Xu Kaimin Zheng Yiu-Tung Poon Nung-Sing Sze Ping Xu Bei Zeng Lijian Zhang National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and Manipulation (Ministry of Education) College of Engineering and Applied Sciences and School of Physics Nanjing University Nanjing 210093 China Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China Department of Mathematics & Statistics University of Guelph Guelph N1G 2W1 Ontario Canada Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Ontario Canada Department of Mathematics Iowa State University Ames Iowa 50011 USA Department of Applied Mathematics The Hong Kong Polytechnic University 999077 Hong Kong China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 China Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon 999077 Hong Kong China

In quantum mechanics, geometry has been demonstrated as a useful tool for inferring nonclassical behaviors and exotic properties of quantum systems. One standard approach to illustrate the geometry of quantum systems is to project the quantum state space onto the Euclidean space via measurements of observables on the system. Despite the great success of this method in studying two-level quantum systems (qubits) with the celebrated Bloch sphere representation, it is still difficult to reveal the geometry of multidimensional quantum systems. Here we report the first experiment measuring the geometry of such projections beyond the qubit. Specifically, we observe the joint numerical ranges of a triple of observables in a three-level photonic system, providing a complete classification of these ranges. We further show that the geometry of different classes reveals ground-state degeneracies of a Hamiltonian as a linear combination of the observables, which is related to quantum phases in the thermodynamic limit. Our results offer a versatile geometric approach for exploring the properties of higher-dimensional quantum systems.

关键词： Geometric & topological phases Quantum foundations Quantum measurements Quantum phase transitions

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MalCommunity: A Graph-Based Evaluation Model for Malware Family Clustering

MalCommunity: A Graph-Based Evaluation Model for Malware Fam...

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2018国际计算机前沿大会（原国际青年计算机大会）

作者： Yihang Chen Fudong Liu Zheng Shan Guanghui Liang State Key of Laboratory of Mathematical Engineering and Advanced Computing

Malware clustering analysis plays an important role in largescale malware homology analysis. However, the generation approach of the ground truth data is usually ignored. The Labels from Anti-virus(AV)engines are most commonly used but some of them are inaccurate or inconsistent. To overcome the drawback, many researchers make ground truth data based on voting mechanism such as AVclass, but this method is difficult to evaluate different-granularity clustering results. Graphbased method like VAMO is more robust but it needs to maintain a largesize database. In this paper, we propose a novel evaluation model named MalCommunity based on the graph named Malware Relation Graph. Different from VAMO, the construction of the graph is free from a largesize database and just needs the AV label information of the samples in the test set. We introduce community detection algorithm Fast Newman to divide the sample set and use modularity parameter to measure the target clustering results. The experiment results indicate that our model has the ability of noise immunity of malware family classification inconsistency and granularity inconsistency from AV labels. Our model is also convenient to evaluate different-granularity clustering methods with different heights.

关键词：

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Text clustering algorithm based on deep representation learning

Text clustering algorithm based on deep representation learn...

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作者： Wang, Binyu Liu, Wenfen Lin, Zijie Hu, Xuexian Wei, Jianghong Liu, Chun State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541000 China

Text clustering is an important method for effectively organising, summarising, and navigating text information. However, in the absence of labels, the text data to be clustered cannot be used to train the text representation model based on deep learning. To address the problem, an algorithm of text clustering based on deep representation learning is proposed using the transfer learning domain adaptation and the parameters update during cluster iteration. First, source domain data is used to perform the pre-training of the deep learning classification model. This procedure acts as an initialisation of the model parameters. Then, the domain discriminator is added to the model, to domain-divide the input sample. If the discriminator cannot distinguish which domain the data belongs to, the common feature space of two domains is obtained, so the domain adaptation problem is solved. Finally, the text feature vectors obtained by the model are clustered with MCSKM++ algorithm. The algorithm not only resolves the model pre-training problem in unsupervised clustering, but also has a good clustering effect on the transfer problem caused by different numbers of domain labels. Experiments suggest that the clustering accuracy of the algorithm is superior to other similar algorithms. © 2018 Institution of engineering and Technology. All rights reserved.

关键词： Iterative methods

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Device-Independent-quantum-randomness-enhanced zero-knowledge proof

arXiv

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arXiv 2021年

作者： Li, Cheng-Long Zhang, Kai-Yi Zhang, Xingjian Yang, Kui-Xing Han, Yu Cheng, Su-Yi Cui, Hongrui Liu, Wen-Zhao Li, Ming-Han Liu, Yang Bai, Bing Dong, Hai-Hao Zhang, Jun Ma, Xiongfeng Yu, Yu Fan, Jingyun Zhang, Qiang Pan, Jian-Wei Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science and Technology of China Hefei230026 China Shanghai Branch CAS Center for Excellence Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Shanghai Research Center for Quantum Sciences Shanghai201315 China Shanghai Jiao Tong University Shanghai200240 China Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Shenzhen Institute for Quantum Science and Engineering Department of Physics Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou450001 China Jinan Institute of Quantum Technology Jinan250101 China

Zero-knowledge proof (ZKP) is a fundamental cryptographic primitive that allows a prover to convince a verifier of the validity of a statement without leaking any further information[1]. As an efficient variant of ZKP, non-interactive zero-knowledge proof (NIZKP) adopting the Fiat-Shamir heuristic is essential to a wide spectrum of applications, such as federated learning[2], blockchain and social networks[3]. However, the heuristic is typically built upon the random oracle model making ideal assumptions about hash functions, which does not hold in reality and thus undermines the security of the protocol[4]. Here, we present a quantum resolution to the problem. Instead of resorting to a random oracle model, we implement a quantum randomness service. This service generates random numbers certified by the loophole-free Bell test[5, 6] and delivers them with post-quantum cryptography (PQC) authentication[7]. Employing this service, we conceive and implement a NIZKP of the three-colouring problem[8]. By bridging together three prominent research themes, quantum non-locality, PQC and ZKP, we anticipate this work to open a new paradigm of quantum information science. Copyright © 2021, The Authors. All rights reserved.

关键词： Hash functions

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Loop-nest Auto-vectorization Method Based on Benefit Analysis

Loop-nest Auto-vectorization Method Based on Benefit Analysi...

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作者： Yao Jinyang Zhao Rongcai Wang Qi Tao Xiaohan State Key Laboratory of Mathematical Engineering and Advanced Computing

The SIMD(Single Instruction Multiple Data) extension component has been widely used in modern processors to improve the execution efficiency of the *** mainstream compilers(such as GCC,ICC,OPEN64,etc.) have supported the SIMD automatic *** current compilers can only vectorize the innermost layer of the loop,but this way often does not get the optimal performance *** at this kind of problem and combining with the processor architecture,a loopnest vectorization method based on the benefit analysis model is proposed and implemented in the GCC *** method can analyze different loop-nest vectorization schemes effectively and choose the most profitable one as the final optimization *** article first reviewed the researches about loop-nest vectorization,then introduces this new code generation method for loop-nest ***,we use several common application examples for *** experimental results show that the results of the benefit model are similar to the actual test results,and accordingly increase the efficiency for loop-nest vectorization.

关键词： Vectorization Loop-nest Benefit Analysis Cost Model

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Geometrical reconstruction of fluorescence events observed by the LHAASO experiment

引用

Chinese Physics C 2021年第4期45卷 416-425页

作者： F.Aharonian Q.An Axikegu L.X.Bai Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi H.Cai J.T.Cai Z.Cao Z.Cao J.Chang J.F.Chang X.C.Chang B.M.Chen J.Chen L.Chen L.Chen L.Chen M.J.Chen M.L.Chen Q.H.Chen S.H.Chen S.Z.Chen T.L.Chen X.L.Chen Y.Chen N.Cheng Y.D.Cheng S.W.Cui X.H.Cui Y.D.Cui B.Z.Dai H.L.Dai Z.G.Dai Danzengluobu D.della Volpe B.DEtorre Piazzoli X.J.Dong J.H.Fan Y.Z.Fan Z.X.Fan J.Fang J.Fang C.F.Feng L.Feng S.H.Feng Y.L.Feng B.Gao C.D.Gao Q.Gao W.Gao M.M.Ge L.S.Geng G.H.Gong Q.B.Gou M.H.Gu J.G.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.Han H.H.He H.N.He J.C.He S.L.He X.B.He Y.He M.Heller Y.K.Hor C.Hou X.Hou H.B.Hu S.Hu S.C.Hu X.J.Hu D.H.Huang Q.L.Huang W.H.Huang X.T.Huang Z.C.Huang F.Ji X.L.Ji H.Y.Jia K.Jiang Z.J.Jiang C.Jin D.Kuleshov K.Levochkin B.B.Li C.Li C.Li F.Li H.B.Li H.C.Li H.Y.Li J.Li K.Li W.L.Li X.Li X.Li X.R.Li Y.Li Y.Z.Li Z.Li Z.Li E.W.Liang Y.F.Liang S.J.Lin B.Liu C.Liu D.Liu H.Liu H.D.Liu J.Liu J.L.Liu J.L.Liu J.S.Liu J.Y.Liu M.Y.Liu R.Y.Liu S.M.Liu W.Liu Y.N.Liu Z.X.Liu W.J.Long R.Lu H.K.Lv B.Q.Ma L.L.Ma X.H.Ma J.R.Mao A.Masood W.Mitthumsiri T.Montaruli Y.C.Nan B.Y..Pang P.Pattarakijwanich Z.Y.Pei M.Y.Qi D.Ruffolo V.Rulev A.Saiz L.Shao O.Shchegolev X.D.Sheng J.R.Shi H.C.Song Yu.V.Stenkin V.Stepanov Q.N.Sun X.N.Sun Z.B.Sun P.H.T.Tam Z.B.Tang W.W.Tian B.D.Wang C.Wang H.Wang H.G.Wang J.C.Wang J.S.Wang L.P.Wang L.Y.Wang R.N.Wang W.Wang W.Wang X.G.Wang X.J.Wang X.Y.Wang Y.D.Wang Y.J.Wang Y.P.Wang Z.Wang Z.Wang Z.H.Wang Z.X.Wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu H.R.Wu S.Wu W.X.Wu X.F.Wu S.Q.Xi J.Xia J.J.Xia G.M.Xiang G.Xiao H.B.Xiao G.G.Xin Y.L.Xin Y.Xing D.L.Xu R.X.Xu L.Xue D.H.Yan C.W.Yang F.F.Yang J.Y.Yang L.L.Yang M.J.Yang R.Z.Yang S.B.Yang Y.H.Yao Z.G.Yao Y.M.Ye L.Q.Yin N.Yin X.H.You Z.Y.You Y.H.Yu Q.Yuan H.D.Zeng T.X.Zeng W.Zeng Z.K.Zeng M.Zha X.X.Zhai B.B.Zhang H.M.Zhang H.Y.Zhang J.L.Zhang J.W.Zhang L.Zhang L.Zhang L.X.Zhang P.F.Zhang P.F.Zhang R.Zhang S.R.Zhang S.S.Zhang X.Zhang X.P.Zhang Y.Zhang Y.Zhang Y.F.Zhang Y.L.Zhang B.Zhao J.Zhao L.Zhao L.Z.Zhao S.P.Zhao F.Zheng Y.Zheng B.Zhou H.Zhou J.N.Zhou P Key Laboratory of Particle Astrophyics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China TIANFU Cosmic Ray Research Center ChengduSichuanChina State Key Laboratory of Paricle Detection and Electronics China University of Science and Technology of China HefeiAnhui 230026China Department of Engineering Physics Tsinghua UniversityBeijing 100084China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China National Space Science Center Chinese Academy of SciencesBeijing 100190China School of Physics Peking UniversityBeijing 100871China Center for Astrophysics Guangzhou UniversityGuangzhouGuangdong 510006China School of Physics and Astronomy&School of Physics(Guangzhou) Sun Yat-sen UniversityZhuhaiGuangdong 519000China School of Physical Science and Technology Guangxi UniversityNanningGuangxi 530004China Hebei Normal University ShijiazhuangHebei 050024China School of Physics and Microelectronics Zhengzhou UniversityZhengzhouHenan 450001China School of Astronomy and Space Science Nanjing UniversityNanjingJiangsu 210023China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjingJiangsu 210023China Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdaoShandong 266237China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China School of Physical Science and Technology Southwest Jiaotong UniversityChengduSichuan 610031China College of Physics Sichuan UniversityChengduSichuan 610065China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasaTibet 850000China School of physics and technology Wuhan UniversityWuhanHubei 430072Ch

The LHAASO-WFCTA experiment,which aims to observe cosmic rays in the sub-EeV range using the fluorescence technique,uses a new generation of high-performance *** ensure that the experiment has ex-cellent detection capability associated with the measurement of the energy spectrum,the primary composition of cosmic rays,and so on,an accurate geometrical reconstruction of air-shower events is *** paper de-scribes the development and testing of geometrical reconstruction for stereo viewed events using the WFCTA(Wide Field of view Cherenkov/Fluorescence Telescope Array)*** approaches,which take full advantage ofthe WFCTA *** *** is the stereo-angular method,which uses the pointing of triggered SiPMs in the shower trajectory,and the other is the stereo-timing method,which uses the triggering time of the fired *** results show that both methods have good geometrical resolution:the resolution of the stereo-timing method is slightly better than the stereo-angular method because the resolution of the latter is slightly limited by the shower track length.

关键词： cosmic ray fluorescence telescope stereo observation geometrical reconstruction

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A Scalable Attention Mechanism Based Neural Network for Text Classification

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Journal of Physics: Conference Series 2020年第2期1486卷

作者： Jianyun Zheng Jianmin Pang Xiaochuan Zhang Di Sun Xin Zhou Kai Zhang Dong Wang MingLiang Li Jun Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450002 China Xi'an Institute of Surveying and Mapping Xi'an 710000 China

In general, deep learning based text classification methods are considered to be effective but tend to be relatively slow especially for model training. In this work, we present a powerful, so-called 'scalable attention mechanism', which performs better than conventional attention mechanism in terms of both effectiveness and the speed of model training. Based on the scalable attention mechanism, we propose a neural network for text classification. The experimental results on eight representative datasets show that our method can obtain similar accuracy to state-of-the-art methods with training in less than 4 minutes on an NVIDIA GTX 1080Ti GPU. To the best of our knowledge, our method is at least twice faster than all the published deep learning classifiers.

关键词：

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Observation of the Crab Nebula with LHAASO-KM2A−a performance study

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Chinese Physics C 2021年第2期45卷 518-530页

作者： F.Aharonian Q.An Axikegu L.X.Bai Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi H.Cai J.T.Cai Z.Cao J.Chang J.F.Chang X.C.Chang B.M.Chen J.Chen L.Chen M.J.Chen M.L.Chen Q.H.Chen S.H.Chen S.Z.Chen T.L.Chen X.L.Chen Y.Chen N.Cheng Y.D.Cheng S.W.Cui X.H.Cui Y.D.Cui B.Z.Dai H.L.Dai Z.G.Dai D.della Volpe B.D'Ettorre Piazzoli X.J.Dong J.H.Fan Y.Z.Fan Z.X.Fan J.Fang K.Fang C.F.Feng L.Feng S.H.Feng Y.L.Feng B.Gao C.D.Gao Q.Gao W.Gao M.M.Ge L.S.Geng G.H.Gong Q.B.Gou M.H.Gu J.G.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.Han H.H.He H.N.He J.C.He S.L.He X.B.He Y.He M.Heller Y.K.Hor C.Hou X.Hou H.B.Hu S.Hu S.C.Hu X.J.Hu D.H.Huang Q.L.Huang W.H.Huang X.T.Huang Z.C.Huang F.Ji X.L.Ji H.Y.Jia K.Jiang Z.J.Jiang C.Jin D.Kuleshov K.Levochkin B.B.Li C.Li F.Li H.B.Li H.C.Li H.Y.Li J.Li K.Li W.L.Li X.Li X.R.Li Y.Li Y.Z.Li Z.Li E.W.Liang Y.F.Liang S.J.Lin B.Liu C.Liu D.Liu H.Liu H.D.Liu J.Liu J.L.Liu J.S.Liu J.Y.Liu M.Y.Liu R.Y.Liu S.M.Liu W.Liu Y.N.Liu Z.X.Liu W.J.Long R.Lu H.K.Lv B.Q.Ma L.L.Ma X.H.Ma J.R.Mao A.Masood W.Mitthumsiri T.Montaruli Y.C.Nan B.Y.Pang P.Pattarakijwanich Z.Y.Pei M.Y.Qi D.Ruffolo V.Rulev A.Sáiz L.Shao O.Shchegolev X.D.Sheng J.R.Shi H.C.Song Yu.V.Stenkin V.Stepanov Q.N.Sun X.N.Sun Z.B.Sun P.H.T.Tam Z.B.Tang W.W.Tian B.D.Wang C.Wang H.Wang H.G.Wang J.C.Wang J.S.Wang L.P.Wang L.Y.Wang R.N.Wang W.Wang X.G.Wang X.J.Wang X.Y.Wang Y.D.Wang Y.J.Wang Y.P.Wang Z.Wang Z.H.Wang Z.X.Wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu H.R.Wu S.Wu W.X.Wu X.F.Wu S.Q.Xi J.Xia J.J.Xia G.M.Xiang G.Xiao H.B.Xiao G.G.Xin Y.L.Xin Y.Xing D.L.Xu R.X.Xu L.Xue D.H.Yan C.W.Yang F.F.Yang J.Y.Yang L.L.Yang M.J.Yang R.Z.Yang S.B.Yang Y.H.Yao Z.G.Yao Y.M.Ye L.Q.Yin N.Yin X.H.You Z.Y.You Y.H.Yu Q.Yuan H.D.Zeng T.X.Zeng W.Zeng Z.K.Zeng M.Zha X.X.Zhai B.B.Zhang H.M.Zhang H.Y.Zhang J.L.Zhang J.W.Zhang L.Zhang L.X.Zhang P.F.Zhang P.P.Zhang R.Zhang S.R.Zhang S.S.Zhang X.Zhang X.P.Zhang Y.Zhang Y.F.Zhang Y.L.Zhang B.Zhao J.Zhao L.Zhao L.Z.Zhao S.P.Zhao F.Zheng Y.Zheng B.Zhou H.Zhou J.N.Zhou P.Zhou R.Zhou X.X.Zhou C.G.Zhu F.R.Zhu H.Zhu K.J.Zhu X.Zuo Key Laboratory of Particle Astrophyics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of Sciences100049 BeijingChina University of Chinese Academy of Sciences 100049 BeijingChina TIANFU Cosmic Ray Research Center ChengduSichuanChina State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China 230026 HefeiAnhuiChina Department of Engineering Physics Tsinghua University100084 BeijingChina National Astronomical Observatories Chinese Academy of Sciences100101 BeijingChina National Space Science Center Chinese Academy of Sciences100190 BeijingChina School of Physics Peking University100871 BeijingChina Center for Astrophysics Guangzhou University510006 GuangzhouGuangdongChina School of Physics and Astronomy&School of Physics(Guangzhou) Sun Yat-sen University519000 ZhuhaiGuangdongChina School of Physical Science and Technology Guangxi University530004 NanningGuangxiChina Hebei Normal University 050024 ShijiazhuangHebeiChina School of Physics and Microelectronics Zhengzhou University450001 ZhengzhouHenanChina School of Astronomy and Space Science Nanjing University210023 NanjingJiangsuChina Key Laboratory of Dark Matter and Space Astronomy Purple Mountain ObservatoryChinese Academy of Sciences210023 NanjingJiangsuChina Institute of Frontier and Interdisciplinary Science Shandong University266237 QingdaoShandongChina Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of Sciences200030 ShanghaiChina Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong University200240 ShanghaiChina School of Physical Science and Technology&School of Information Science and Technology Southwest Jiaotong University610031 ChengduSichuanChina College of Physics Sichuan University610065 ChengduSichuanChina Key Laboratory of Cosmic Rays(Tibet University) Ministry of Education850000 LhasaTibetChina School of Physics and Tec

A sub-array of the Large High Altitude Air Shower Observatory(LHAASO),KM2A is mainly designed to observe a large fraction of the northern sky to hunt for γ-ray sources at energies above 10 *** though the detector construction is still underway,half of the KM2A array has been operating stably since the end of *** this paper,we present the KM2A data analysis pipeline and the first observation of the Crab Nebula,a standard candle in very high energy γ-ray *** detect γ-ray signals from the Crab Nebula in both energy ranges of 10-100 TeV and>100 TeV with high significance,by analyzing the KM2A data of 136 live days between December 2019 and May *** the observations,we test the detector performance,including angular resolution,pointing accuracy and cosmic-ray background rejection *** energy spectrum of the Crab Nebula in the energy range 10-250 TeV fits well with a single power-law function dN/dE=(1.13±0.05stat±0.08sys)×10^(-14).(E/20 TeV)-309±0.06stat±0.02syscm^(-2) s^(-1) TeV^(-1).It is consistent with previous measurements by other *** opens a new window of γ-ray astronomy above 0.1 PeV through which new ultrahigh-energy γ-ray phenomena,such as cosmic PeVatrons,might be discovered.

关键词： γ-ray Crab Nebula extensive air showers cosmic rays

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