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arXiv 2025年

作者： Hébert-Dufresne, Laurent Ahn, Yong-Yeol Allard, Antoine Crothers, Jessica W. Dodds, Peter Sheridan Galesic, Mirta Ghanbarnejad, Fakhteh Gravel, Dominique Hammond, Ross A. Lerman, Kristina Lovato, Juniper Openshaw, John J. Redner, S. Scarpino, Samuel V. St-Onge, Guillaume Tangherlini, Timothy R. Young, Jean-Gabriel Vermont Complex Systems Institute University of Vermont BurlingtonVT United States Translational Global Infectious Disease Research Center University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Département de physique de génie physique et d’optique Université Laval QuébecQC Canada Department of Pathology and Laboratory Medicine Robert Larner M.D. College of Medicine University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States Complexity Science Hub Vienna Austria School of Technology and Architecture SRH University of Applied Sciences Heidelberg Leipzig Germany Département de biologie Université de Sherbrooke SherbrookeQC Canada Brown School Washington University in St. Louis St. LouisMO United States The Brookings Institution WashingtonDC United States Information Sciences Institute University of Southern California United States Division of Infectious Diseases and Geographic Medicine Stanford University StanfordCA United States Institute for Experiential AI Northeastern University BostonMA United States Laboratory for the Modeling of Biological and Socio-technical Systems Northeastern University BostonMA United States The Roux Institute Northeastern University PortlandME United States Department of Scandinavian School of Information University of California Berkeley BerkeleyCA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions. Copyright © 2025, The Authors. All rights reserved.

关键词： Histology

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UAV-assisted intelligent reflecting surface symbiotic radio system

arXiv

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arXiv 2020年

作者： Hua, Meng Yang, Luxi Wu, Qingqing Pan, Cunhua Li, Chunguo Swindlehurst, A. Lee School of Information Science and Engineering Southeast University Nanjing210096 China State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau Macao999078 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Center for Pervasive Communications and Computing University of California at Irvine IrvineCA92697 United States

This paper investigates a symbiotic unmanned aerial vehicle (UAV)-assisted intelligent reflecting surface (IRS) radio system, where the UAV is leveraged to help the IRS reflect its own signals to the base station, and meanwhile enhance the UAV transmission by passive beamforming at the IRS. First, we consider the weighted sum-rate maximization problem among all IRSs by jointly optimizing the UAV trajectory, IRS phase shift matrix, and IRS scheduling, subject to the minimum primary rate requirements. To tackle this complicated problem, a relaxation-based algorithm is proposed. We prove that the converged relaxation scheduling variables are binary, which means that no reconstruct strategy is needed, and thus the UAV rate constraints are automatically satisfied. Second, we consider the problem of maximizing the minimum rate of the IRSs. We find that the relaxation-based method cannot solve this max-min problem since the minimum primary rate requirements may not be satisfied by the binary reconstruction operation. To address this issue, we first transform the binary constraints into a series of equivalent equality constraints. Then, a penalty-based algorithm is proposed to obtain a suboptimal solution. Numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmarks. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Enhancing countries' fitness with recommender systems on the international trade network

arXiv

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arXiv 2019年

作者： Liao, Hao Wu, Xing-Tong Vidmer, Alexandre Huang, Xiao-Min Liu, Ming-Kai Zhou, Mingyang Zhang, Yi-Cheng National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Physics University of Fribourg Fribourg1700 Switzerland

Prediction is one of the major challenges in complex systems. The prediction methods have shown to be effective predictors of the evolution of networks. These methods can help policy makers to solve practical problems successfully and make better strategy for the future. In this work, we focus on exporting countries' data of the international trading network. A recommendation system is then used to identify the products corresponding to the production capacity of each individual country, but are somehow overlook by the country. Then, we simulate the evolution of the country's fitness if it would have followed the recommendations. The result of this work is the combination combine these two methods to provide insights to countries on how to enhance the diversification of their exported products in a scientific way and improve national competitiveness significantly, especially for developing countries. Copyright © 2019, The Authors. All rights reserved.

关键词： Recommender systems

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D2TFRS: An object recognition method for autonomous vehicles based on RGB and spatial values of pixels 1st

引用

1st International Conference on Infrastructure, Technologies and Applications, SCITA 2017

作者： Alam, Furqan Mehmood, Rashid Katib, Iyad Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia High Performance Computing Center King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9783319941790

Autonomous driving is now near future reality which will transform our world due to its numerous benefits. The foremost challenge to this task is to correctly identify the objects in the driving environment. In this work, we propose an object recognition method known as Decision Tree and Decision Fusion based Recognition System (D2TFRS) for autonomous driving. We combined two separate feature sets, which are RGB pixel values and spatial points X,Y of each pixel to form our dataset. The D2TFRS is based on our intuition that reclassification of pre-identified misclassified objects in a driving environment can give better prediction accuracy. Results showed that D2TFRS outperformed AdaBoost classifier and performed better than C5.0 classifier in terms of the classification accuracy and Kappa. In terms of speed, C5.0 outperforms both AdaBoost and D2TFRS. However, D2TFRS outperformed AdaBoost with respect to speed. We strongly believe that D2TFRS will have better parallelization performance compared to the other two methods and it will be investigated in our future work. © 2018, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Object recognition

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Intelligent reflecting surface based passive information transmission: A symbol-level precoding approach

arXiv

引用

arXiv 2020年

作者： Liu, Rang Li, Ming Liu, Qian Swindlehurst, A. Lee Wu, Qingqing School of Information and Communication Engineering Dalian University of Technology Dalian116024 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China Center for Pervasive Communications and Computing University of California IrvineCA92697 United States State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau 999078 China

Intelligent reflecting surfaces (IRS) have been proposed as a revolutionary technology owing to its capability of adaptively reconfiguring the propagation environment in a cost-effective and hardware-efficient fashion. While the application of IRS as a passive reflector to enhance the performance of wireless communications has been widely investigated in the literature, using IRS as a passive transmitter recently is emerging as a new concept and attracting steadily growing interest. In this paper, we propose two novel IRS-based passive information transmission systems using advanced symbol-level precoding. One is a standalone passive information transmission system, where the IRS operates as a passive transmitter serving multiple receivers by adjusting its elements to reflect unmodulated carrier signals. The other is a joint passive reflection and information transmission system, where the IRS not only enhances transmissions for multiple primary information receivers (PIRs) by passive reflection, but also simultaneously delivers additional information to a secondary information receiver (SIR) by embedding its information into the primary signals at the symbol level. Two typical optimization problems, i.e., power minimization and quality-of-service (QoS) balancing, are investigated for the proposed IRS-based passive information transmission systems. Simulation results demonstrate the feasibility of IRS-based passive information transmission and the effectiveness of our proposed algorithms, as compared to other benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Transmitters

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Parallelization of protein clustering algorithm using OpenMP 1

引用

2nd International Conference on Advances in computing and Data sciences, ICACDS 2018

作者： Dhar, Dhruv Hegde, Lakshana Patil, Mahesh S. Chickerur, Satyadhyan Department of Computer Science and Engineering B.V.B College of Engineering and Technology Centre for High Performance Computing KLE Technological University HubballiKarnataka580031 India

ISBN: (数字)9789811318139

ISBN: (纸本)9789811318122

Proteins are the building blocks of all living organisms and its analysis can help us to understand the bimolecular mechanics of living organisms. Protein clustering attempts to group similar protein sequences and has diverse applications in bioinformatics. However, this operation faces various computational challenges because of dependency on complex data structures, high memory usage and irregular memory access patterns. In genome studies, the time consideration for alignment is also an important parameter and should be minimized. Conventional solutions have rather been unsuccessful in achieving decent runtime performance because these algorithms are designed for serial computation which means that they use a single processor to perform computations. These algorithms can be improved upon by modifying them to use multiple processing elements. The purpose of this research is to modify existing protein clustering algorithm and apply parallelization techniques on them in order to optimize protein sequencing operation for faster results without sacrificing accuracy. © Springer Nature Singapore Pte Ltd 2018.

关键词： Proteins

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A framework for faster porting of scientific applications between heterogeneous clouds 1st

引用

1st International Conference on Infrastructure, Technologies and Applications, SCITA 2017

作者： Ahmed, Waseem Khan, Mohsin Khan, Adeel Ahmed Mehmood, Rashid Algarni, Abdullah Albeshri, Aiiad Katib, Iyad Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science and Engineering HKBK College of Engineering Visvesvaraya Technological University Bangalore India High Performance Computing Center King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9783319941790

The emergence of pay-as-you-use compute clouds has enabled scientists to experiment with the latest processor architectures and accelerators. However, the lack of standardization in cloud computing, more specifically in the interoperability context, makes the task of portability of applications between clouds challenging. Two main tasks that users of multi-vendor clouds will need to perform are porting cost analysis and faster source-to-source translation. Cost analysis is essential to help evaluate the feasibility and cost of portability. And any automation of the source-to-source translation step will help developers perform the translation faster while taking advantage of platform-specific features. This paper presents a framework that assists a developer in performing these two tasks. The first task is achieved using the Maintainability Analyzer module which generates unique funnel shaped patterns that give an insight about the maintainability of an application and its potential for porting. Different scientific applications from various domains, that were developed using different programming paradigms, were evaluated using this module. For the second task, a set of modules use a knowledge repository to perform source-to-source translations while ensuring the maintainability of the generated code. The framework has been tested with different architecture and library combinations with promising results. © 2018, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Cost benefit analysis

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Ergodicity of the 2D Navier-Stokes Equations with Degenerate Multiplicative Noise

引用

Acta Mathematicae Applicatae Sinica 2018年第1期34卷 97-118页

作者： Zhao DONG Xu-hui PENG RCSDS Academy of Mathematics and Systems Science Chinese Academy of Sciences China Key Laboratory of High Performance Computing and Stochastic Information Processing (HPCSIP) (Ministry of Education of China) College of Mathematics and Computer Science Hunan Normal University Changsha 410081. China School of Mathematics Sciences University of Chinese Academy of Sciences Beijing 100190 China

Consider the two-dimensional, incompressible Navier-Stokes equations on torus T^2= [-π, π]^2 driven by a degenerate multiplicative noise in the vorticity formulation（abbreviated as SNS）： dwt = ν？w_tdt ＋B（Kw_t, w_t）dt ＋ Q（w_t）dW t. We prove that the solution to SNS is continuous differentiable in initial value. We use the Malliavin calculus to prove that the semigroup{P_t}_t≥0 generated by the SNS is asymptotically strong Feller. Moreover, we use the coupling method to prove that the solution to SNS has a weak form of *** almost the same Hypotheses as that given by Odasso, Prob. Theory Related Fields, 140： 41–82（2005）with a different method, we get an exponential ergodicity under a stronger norm.

关键词： tochastic Navier-Stokes equation asymptotically strong Feller property ergodicity

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Supernova Neutrino Detection with LHAASO-MD 37

Supernova Neutrino Detection with LHAASO-MD

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Liu, D. Chang, J. Chen, S. Dai, H. Feng, C. Gao, B. Gong, G. Gu, M. Li, F. Ma, X. Wang, X. Wang, Z. Zuo, X. Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D’Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y. 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.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China National University of Defense Technology Hunan Changsha410073 China Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Tsinghua University Beijing100084 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 2 Dublin 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 College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 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 Zhuhai519000 China Dipartimento di Fisica Università di Napoli `‘Federico II" Complesso Universit

The core-collapse supernova releases a tremendous number of neutrinos, which can provide insight into many research areas, including particle physics, astrophysics, nuclear physics, and cosmology. We can detect the signal through a positron produced from the inverse beta decay (IBD) interaction between the electron antineutrino and water. The Large high Altitude Air Shower Observatory[1] muon detector (LHAASO-MD) with 51-kton water can serve this purpose. The MD detectors have been designed to have a scattered layout as well as spatial uniformity. We hope to design a dedicated supernova trigger system in the data acquisition system to take advantage of these unique detector characteristics. The large numbers of MeV-scale supernova burst neutrinos can be observed from a collective rise in all photomultiplier rates on top of the dark noise. This system should effectively suppress the cosmic ray background, optimizes the neutrino detection sensitivity, and realizes the supernova neutrino detection by optimizing the online trigger, data acquisition, and offline data analysis at LHAASO. The trigger system is estimated to be fully sensitive to 1987A-type supernova bursts throughout most of the Milky Way and can eventually help LHAASO join the SuperNova Early Warning System (SNEWS). © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Supernovae

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Search for very high energy γ-ray emission from GRB 190829A with LHAASO-WCDA triggerless data 37

Search for very high energy γ-ray emission from GRB 190829A...

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Yao, Yuhua Liu, Wei Qiao, Bingqiang Guo, Yiqing Hu, Hongbo Yang, Chaowen Yao, Zhiguo Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D’Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y. 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.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. College of Physical Science and Technology Sichuan University N.29 Wangjiang Road Jiuyanqiao Wuhou District Sichuan Province Chengdu City610064 China Institute of High Energy Physics Chinese Academy of Sciences No.19B Yuquan Road Shijingshan District Beijing100049 China University of Chinese Academy of Sciences 19 A Yuquan Rd Shijingshan District Beijing100049 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 2 Dublin 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 College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 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 Zhuhai519000 China

The observation of very high energy (VHE, > 100GeV) photon from γ-ray bursts (GRBs) can advance our understanding of their radiation mechanism, the evolution of host-galaxies, violations of Lorentz invariance, extragalactic background light, and intergalactic magnetic field. Recently the measurements on VHE emission from afterglow phase have blossomed and borne fruit while detection of the prompt emission is rare. It’s important to observe prompt emissions by ground-based instruments which take advantage of wide field of view and high duty cycle. Up to now, of the total six GRBs reported with VHE γ-ray emissions, GRB 190829A is the nearest one. The detection of its VHE emission was announced by H.E.S.S. at about 4h 20m after the burst. While during its prompt phase, GRB 190829A was at the edge of the field of view of LHAASO. Benefiting by the high altitude of 4410 m and the triggerless observation mode, the energy threshold of LHAASO is largely reduced to tens of GeV. This work presents the search for VHE γ-ray emission from GRB 190829A with the triggerless data taken by the first pond of LHAASO-WCDA. The analysis shows no indication of VHE emission neither in the prompt nor in the first 2 hours of afterglow phase, thus 95% upper limits are yielded. Furthermore, the upper limits of prompt phase was used to constrain the spectral parameter space of VHE extra component based on a phenomenological model. © Copyright owned by the author(s).

关键词： Gamma rays

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