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Self-calibration of LHAASO-KM2A electromagnetic particle detectors using single particles within extensive air showers

引用

Physical Review D 2022年第12期106卷 122004-122004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 J. T. Cai Z. Cao J. Chang J. F. Chang E. S. Chen L. Chen M. J. Chen M. L. Chen Q. H. Chen S. H. Chen S. Z. Chen T. L. Chen Y. Chen H. L. Cheng N. Cheng Y. D. Cheng S. W. Cui X. H. Cui Y. D. Cui B. D’Ettorre Piazzoli B. Z. Dai H. L. Dai Z. G. Dai Danzengluobu D. della Volpe K. K. Duan J. H. Fan Y. Z. Fan Z. X. Fan J. Fang K. Fang C. F. Feng L. Feng S. H. Feng X. T. Feng Y. L. Feng B. Gao C. D. Gao L. Q. Gao Q. Gao W. Gao W. K. Gao M. M. Ge L. S. Geng G. H. Gong Q. B. Gou M. H. Gu F. L. Guo J. G. Guo X. L. Guo Y. Q. Guo Y. Y. Guo Y. A. Han H. H. He H. N. He S. L. He X. B. He Y. He M. Heller Y. K. Hor C. Hou X. Hou H. B. Hu Q. Hu S. Hu S. C. Hu X. J. Hu D. H. Huang W. H. Huang X. T. Huang X. Y. Huang Y. Huang Z. C. Huang X. L. Ji H. Y. Jia K. Jia K. Jiang Z. J. Jiang M. Jin M. M. Kang T. Ke 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. R. Li X. 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. Liu Y. N. Liu W. J. Long R. Lu Q. Luo H. K. Lv B. Q. Ma L. L. Ma X. H. Ma J. R. Mao A. Masood Z. Min W. Mitthumsiri Y. C. Nan Z. W. Ou B. Y. Pang P. Pattarakijwanich Z. Y. Pei M. Y. Qi Y. Q. Qi B. Q. Qiao J. J. Qin D. Ruffolo A. Sáiz C. Y. Shao L. Shao O. Shchegolev X. D. Sheng J. Y. Shi H. C. Song Yu. V. Stenkin V. Stepanov Y. Su 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. Wang R. N. Wang W. Wang X. G. Wang X. Y. Wang Y. Wang Y. D. Wang Y. J. Wang Y. P. Wang Z. H. Wang Z. X. Wang Z. Wang D. M. Wei J. J. Wei Y. J. Wei T. Wen C. Y. Wu H. R. Wu S. Wu X. F. Wu Y. S. Wu S. Q. Xi J. Xia J. J. Xia G. M. Xiang D. X. Xiao G. Xiao G. G. Xin Y. L. Xin Y. Xing Z. Xiong D. L. Xu R. X. Xu L. Xue D. H. Yan J. Z. Yan C. W. Yang F. F. Yang H. W. Yang J. Y. Yang L. L. Yang M. J. Yang R. Z. Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 69029 Heidelberg Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China 230026 Hefei Anhui China School of Physical Science and Technology & School of Information Science and Technology Southwest Jiaotong University 610031 Chengdu Sichuan China College of Physics Sichuan University 610065 Chengdu Sichuan China Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences 100049 Beijing China TIANFU Cosmic Ray Research Center 610299 Chengdu Sichuan China School of Astronomy and Space Science Nanjing University 210023 Nanjing Jiangsu China Center for Astrophysics Guangzhou University 510006 Guangzhou Guangdong China University of Chinese Academy of Sciences 100049 Beijing China Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences 210023 Nanjing Jiangsu China Key Laboratory of Cosmic Rays (Tibet University) Ministry of Education 850000 Lhasa Tibet China National Astronomical Observatories Chinese Academy of Sciences 100101 Beijing China Hebei Normal University 050024 Shijiazhuang Hebei China School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai) Sun Yat-sen University 519000 Zhuhai & 510275 Guangzhou Guangdong China Dipartimento di Fisica dell’Università di Napoli “Federico II” Complesso Universitario di Monte Sant’Angelo via Cinthia 80126 Napoli Italy School of Physics and Astronomy Yunnan University 650091 Kunming Yunnan China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet 1211 Geneva Switzerland Institute of Frontier and Inte

In the Large High Altitude Air Shower Observatory (LHAASO), the square kilometer array, with 5249 electromagnetic particle detectors (EDs) and 1188 muon detectors, is deployed to explore the gamma-ray sources above 30 TeV with unprecedented sensitivity and to measure primary cosmic rays in the energy range from 10 TeV to 100 PeV. The energetic particles produced by extensive air showers can serve as a continuously available source for calibration of the numerous EDs over a large area. In this study, the detector untriggered probability is first proposed to estimate the particle density at different distances from the shower core and distinguish the characteristic single-particle signal detected by each ED. This method uses science data directly, and does not require prior knowledge of the cosmic-ray elemental composition or hadronic interaction model. Experimental results show that this self-calibration can be used to determine the number of particles detected by each ED with an accuracy better than 2% within a time scale of hours, which is adequate to meet the physics requirements of the LHAASO experiment. With this high efficiency and accuracy, this calibration also provides an ideal method to monitor the detector performance throughout an expected lifetime of >10 years.

关键词： Cosmic rays & astroparticles Astrophysical & cosmological simulations Cosmic ray & astroparticle detectors Monte Carlo methods Probability theory Scintillators

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Brief Investigation on Square Root of a Node of <i>T</i><sub>3</sub>Tree

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Advances in Pure Mathematics 2018年第7期8卷 666-671页

作者： Guihong Chen Jianhui Li Department of Computer Science and Technology Neusoft Institute Guangdong Foshan China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

The article investigates some properties of square root of T3 tree’s nodes. It first proves several inequalities that are helpful to estimate the square root of a node, and then proves several theorems to describe th... 详细信息

关键词： Square Root Node Valuated Binary Tree Inequality

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Effect of Oxygen Interstitial Ordering on Multiple Order Parameters in Rare Earth Ferrite

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Physical Review Letters 2019年第24期123卷 247601-247601页

作者： Yang Zhang Wenbin Wang Wandong Xing Shaobo Cheng Shiqing Deng Manuel Angst Chu-Ping Yu Fanli Lan Zhiying Cheng David Mandrus Brian Sales Jian Shen Xiaoyan Zhong Nyan-Hwa Tai Rong Yu Jing Zhu National Center for Electron Microscopy in Beijing Key Laboratory of Advanced Materials (MOE) The State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing 100084 People’s Republic of China Institute of Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Jülich Centre for Neutron Science JCNS and Peter Grünberg Institut PGI JARA-FIT Forschungszentrum Jülich GmbH 52425 Jülich Germany Department of Materials Science and Engineering National Tsing-Hua University Hsinchu 30013 Taiwan State Key Laboratory of Surface Physics and Department of Physics Fudan University Shanghai 200433 China Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA

Oxygen interstitials and vacancies play a key role in modulating the microstructure and properties of nonstoichiometric oxide systems, such as those used for superconductors and multiferroics. key to understanding the tuning mechanisms resulting from oxygen doping is a knowledge of the precise positions of these lattice defects, and of the interaction both between these defects and with many order parameters. Here, we report how such information can, for the first time, be obtained from a sample of LuFe2O4.22 using a range of techniques including advanced electron microscopy, atomic-resolution spectroscopy, and density functional theory calculations. The results provide quantitative atomic details of the crystal unit cell, together with a description of the ferroelastic, ferroelectric, and ferromagnetic order parameters. We elucidate also the interaction between these order parameters and the positions of the oxygen interstitials in the oxygen-enriched sample. The comprehensive analysis of oxygen interstitial ordering provides insights into understanding the coupling among different degrees of freedom in rare earth ferrites and demonstrates that oxygen content regulation is a powerful tool for tuning the microstructure and properties for this class of quantum material.

关键词： Dopants Interstitials Microstructure Order parameters Ferrites Multiferroics Density functional theory Electron diffraction High-resolution electron energy loss spectroscopy High-resolution transmission electron microscopy Scanning transmission electron microscopy

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Impact of temperature and relative humidity on the transmission of COVID-19: A modeling study in China and the United states

arXiv

引用

arXiv 2020年

作者： Wang, Jingyuan Tang, Ke Feng, Kai Lin, Xin Lv, Weifeng Chen, Kun Wang, Fei School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Social Sciences Tsinghua University China State Key Laboratory of Software Development Environment Beihang University China Department of Statistics University of Connecticut United States Center for Population Health University of Connecticut Health Center United States Department of Population Health Sciences Weill Cornell Medical College Cornell University United States

Objectives We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status, and human mobility status. Design A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. Setting We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1,005 U.S. counties. Participants A total of 69,498 cases in China and 740,843 cases in the U.S. are used for calculating the effective reproductive numbers. Primary outcome measures Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value). Results Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the U.S. Conclusions Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1 degree Celsius is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI [-0.0395,-0.0125]) in China and by 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.;an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI [-0.0108,-0.0045]) in China and by 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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Accurate ECG Data Generation with a Simple Generative Adversarial Network

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Journal of Physics: Conference Series 2020年第1期1631卷

作者： Haiyan Wang Zhaoyang Ge Zongmin Wang Collaborative Innovation Centre for Internet Healthcare Zhengzhou University 75 Daxue Road Zhengzhou 450052 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450003 China Simulation Experiment Centre Zhengzhou University of Aeronautics Zhengzhou 450046 China School of Information Engineering Zhengzhou University Zhengzhou 450001 China

Since ECG data is highly sensitive medical data, the acquisition of ECG data is highly restricted. However, with the increasing demand for ECG big data research, the improvement of computer computing capabilities, and the development of deep learning, the direction of ECG intelligent analysis is facing a serious lack of standard clinical data. In order to generate more precise ECG data, this paper proposes a GAN architecture for generating ECG heartbeat data. The network structure is simple and does not require any domain knowledge. In this paper, the MIT-BIH Arrhythmia database is selected, from which all left bundle branch block heartbeats are selected to form a training dataset. The training process shows that the proposed GAN structure is effective and accurate, and the generated results show that not only the generated simulated ECG heartbeat data is diverse, but also highly similar to the real data.

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Improved integral and zero-correlation linear cryptanalysis of CLEFIA block cipher 12th

Improved integral and zero-correlation linear cryptanalysis ...

引用

12th International Conference on Information Security and Cryptology, INSCRYPT 2016

作者： Yi, Wentan Wu, Baofeng Chen, Shaozhen Lin, Dongdai State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783319547046

CLEFIA is a block cipher developed by Sony Corporation in 2007. It is a recommended cipher of CRYPTREC, and has been adopted as ISO/IEC international standard in lightweight cryptography. In this paper, some new 9-round zero-correlation linear distinguishers of CLEFIA are constructed with independent input masks and output masks, which admit multiple zero-correlation linear attacks on 14/15- round CLEAIA-192/256 about 79 times faster than results of the SAC paper with one-eighth of data. Furthermore, some new integral distinguishers over 9 rounds are derived by the relations between integral distinguishers and zero-correlation linear approximations. By using these integral distinguishers, the previous integral attacks on CLEFIA are improved with the partial sum technique. Our results have either one more rounds or lower time complexity than previous attack results with integral and zero-correlation linear cryptanalysis. © Springer International Publishing AG 2017.

关键词： Cryptography

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SARF: A Spark Based Recommendation Framework in the Cloud

SARF: A Spark Based Recommendation Framework in the Cloud

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International Conference on advanced Cloud and Big Data (CBD)

作者： Jieyao Zhou Yifei Lu Ka Bian Lei Qian School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi China

With the development of the IT industry, the information resources and services that people come into contact with have been growing. The sharing and interaction of these two types of information has led to further growth in data resulting in the advent of the era of big data. However, due to the complexity of information, such as low utilization, the confusion, and disorder, it will be a great challenge for service providers to offer customers services with high quality and accurate quality. The recommendation system is a solution adopted by e-marketers for such problems. This paper proposes a SPARK-based recommendation algorithm called SARF. The SARF first reads the original data from the HDFS and uses Spark to yield the datasets. Then, by using these datasets, the optimal recommended model is generated through iterative process. Finally, we gain the results which are recommended to the user by the optimal model. The final experimental results show that our SARF framework can effectively reduce the recommendation error and solve the problem of cold start.

关键词： Collaboration Filtering Sparks Big Data Matrix decomposition Training Filtering algorithms

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Quark transverse spin-momentum correlation of the pion from lattice QCD: The Boer-Mulders function

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Physical Review D 2025年第9期111卷 094507-094507页

作者： Lisa Walter Jun Hua Sebastian Lahrtz Lingquan Ma Andreas Schäfer Hai-Tao Shu Yushan Su Peng Sun Wei Wang Xiaonu Xiong Yi-Bo Yang Jian-Hui Zhang Qi-An Zhang Institut für Theoretische Physik Universität Regensburg D-93040 Regensburg Germany Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou 510006 People’s Republic of China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 People’s Republic of China Center of Advanced Quantum Studies Department of Physics Beijing Normal University Beijing 100875 People’s Republic of China School of Science and Engineering The Chinese University of Hong Kong Shenzhen 518172 People’s Republic of China Key Laboratory of Quark & Lepton Physics (MOE) and Institute of Particle Physics Central China Normal University Wuhan 430079 People’s Republic of China Department of Physics University of Maryland College Park Maryland 20742 USA Institute of Modern Physics Chinese Academy of Sciences Lanzhou Gansu Province 730000 People’s Republic of China INPAC Key Laboratory for Particle Astrophysics and Cosmology (MOE) Shanghai Key Laboratory for Particle Physics and Cosmology School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 People’s Republic of China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 Guangdong Province People’s Republic of China School of Physics Central South University Changsha 418003 People’s Republic of China CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 People’s Republic of China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 People’s Republic of China International Centre for Theoretical Physics Asia-Pacific Beijing/Hangzhou People’s Republic of China University of Chinese Academy of Sciences School of Physical Sciences Beijing 100049 People’s Republic of China School of Physi

We present the first lattice QCD calculation of the quark transverse spin-momentum correlation, i.e., the T-odd Boer-Mulders function of the pion, using large-momentum effective theory. The calculation is done at three lattice spacings a={0.098,0.085,0.064} fm and pion masses ∼350 MeV, with pion momenta up to 1.8 GeV. The matrix elements are renormalized in a state-of-the-art scheme and extrapolated to the continuum and infinite momentum limit. We have implemented the perturbative matching up to the next-to-next-to-leading order and carried out a renormalization-group resummation. Our results provide valuable input for phenomenological analyses of the Boer-Mulders single-spin asymmetry.

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Biological data processing based on bio-processor unit(BPU), a new concept for next generation computational biology

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Science China(Life Sciences) 2018年第5期61卷 597-598页

作者： Di Liu Dongbo Bu Tieliu Shi Jianxiao Quan Depeng Wang Yongyong Shi Xiao-Chen Bo Wenbao Han Computational Virology Group Wuhan Institute of Virology Chinese Academy of Sciences Wuhan 430071 China Key Laboratory of InteUigent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Center for Bioinformatics and Computational Biology and the Institute of Biomedical Sciences School of Life Sciences East China Normal University Shanghai 200214 China State Key Laboratory of Mathematical Engineering and Advanced Computation Wuxi 214122 China Jiangsu Variable Supercomputer Tech Co Ltd. Wuxi 214125 China NextOmics Blosctences Wuhan 430074 China Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education) Shanghai Jiao Tong University Shanghai 200030 China Beijing Institute of Radiation Medicine Beifing 100850 China

The revolution of biotechnology has pushed forward life sciences into the Big Data ***,high-throughput bio-techniques have greatly accelerated the integration of biology,computing and informatics,and hence substantially pushed forward the development of bioinformatics and computational *** to the latest report of data deposition within National Centre for Biotechnology Information（NCBI）,the genome sequencing projects have increased 49.94%.

关键词：计算生物学简历处理器数据基信息科学生物信息学生命科学 NCBI

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Intelligent identification of two-dimensional nanostructures by machine-learning optical microscopy

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Nano Research 2018年第12期11卷 6316-6324页

作者： Xiaoyang Lin Zhizhong Si Wenzhi Fu Jianlei Yang Side Guo Yuan Cao Jin Zhang Xinhe Wang Peng Liu Kaili Jiang Weisheng Zhao Fert Beijing Research Institute School ofMicroelectronics & Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijing 100191China Beihang-GoertekJoint Microelectronics Institute Qingdao Research InstituteBeihang UniversityQingdao 266000China Fert Beijing Research Institute School of Computer Science and Engineering & Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijing 100191China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics & Tsinghua-Foxconn Nanotechnology Research CenterCollaborative Innovation Center of Quantum MatterTsinghua UniversityBeijing 100084China

Two-dimensional (2D) materials and their heterostructures, with wafer-scale synthesis methods and fascinating properties, have attracted significant interest and triggered revolutions in corresponding device applications. However, facile methods to realize accurate, intelligent, and large-area characterizations of these 2D nanostructures are still highly desired. Herein, we report the successful application of machine-learning strategy in the optical identification of 2D nanostructures. The machine-learning optical identification (MOI) method endows optical microscopy with intelligent insight into the characteristic color information of 2D nanostructures in the optical photograph. The experimental results indicate that the MOI method enables accurate, intelligent, and large-area characterizations of graphene, molybdenum disulfide, and their heterostructures, including identifications of the thickness, existence of impurities, and even stacking order. With the convergence of artificial intelligence and nanoscience, this intelligent identification method can certainly promote fundamental research and wafer-scale device applications of 2D nanostructures.

关键词： machine-learning optical identification two-dimensional (2D) material heterostructure artificial intelligence

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