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arXiv 2023年

作者： Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. 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, 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, H.L. Cheng, N. Cheng, Y.D. 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. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. 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.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Long, W.J. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Nan, Y.C. 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. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Z.B. Tian, W.W. Wang, C. Wang, C.B. 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 Chengdu610000 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institute 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 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 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 Zhuhai 519000 China Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Department of Engineering Physics Tsinghua University Beijing1

Some gamma-ray bursts (GRBs) have an afterglow in the tera-electronvolt (TeV) band, but the early onset of this afterglow has not been observed. We report observations with the Large high Altitude Air Shower Observatory of the bright GRB 221009A, which serendipitously occurred within the instrument field of view. More than 64,000 photons (above 0.2 TeV) were detected within the first 3000 seconds. The TeV photon flux began several minutes after the GRB trigger, then rose to a flux peak about 10 seconds later. This was followed by a decay phase, which became more rapid at ∼ 650 s after the peak. The emission can be explained with a relativistic jet model with half-opening angle ∼ 0.8◦, consistent with the core of a structured jet. This interpretation could explain the high isotropic energy of this GRB. Copyright © 2023, The Authors. All rights reserved.

关键词： Photons

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Implementing the Matrix Multiplication with DFC on Kunlun Small Scale computer

Implementing the Matrix Multiplication with DFC on Kunlun Sm...

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IEEE International Conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Zheng Du Jing Zhang Shihao Sha Qiuming Luo Department of Computer Science and Technology Tsinghua University NHPCC/Guangdong Key Laboratory of popular UPC & Guangdong Province Engineering Center of China-made High Performance Data Computing System in College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

In this paper, we demonstrate a new dataflow platform of DFC, which can handle the successive dataflow computing passes with tagged data. By implementing the matrix multiplication in DFC, we show that DFC can exploit the parallelism automatically with a much simple dataflow graph constructed by DF functions of DFC. Different from the other dataflow execution platform, DFC support multiple worker threads for one dataflow node of DF functions. By running the matrix multiplication program of DFC on Kunlun system, it was verified that DFC get a reasonable speedup for large scale computing for thread number up to 512.

关键词： Distributed computing Handheld computers

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Enhancement of optomechanically induced sum sideband using parametric interactions

arXiv

引用

arXiv 2019年

作者： Li, Wen-An Huang, Guang-Yao School of Physics and Electronic Engineering Guangzhou University Guangzhou510006 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China

We theoretically study radiation pressure induced generation of the frequency components at the sum sideband in an optomechanical system containing an optical parametric amplifier (OPA). It is shown that an OPA inside a cavity can considerably enhance the amplitude of sum sideband even with low power input fields. We find a new matching condition for the upper sum sideband generation. The height and width of the new peak can be adjusted by the nonlinear gain of the OPA. Furthermore, the lower sum sideband generation can be enhanced with several orders of magnitude by tuning the nonlinear gain parameter and the phase of the field pumping the OPA. The enhanced sum sideband may have potential applications to the manipulation of light in a on-chip optomechanical device and the sensitively sensing for precision measurement in the weak optomechanical coupling regime. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical parametric amplifiers

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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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Sharpening semantic gradient in a planarized sentence representation

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Neural networks : the official journal of the International Neural Network Society 2025年 190卷 107687页

作者： Caiwei Yang Yanping Chen Shuai Yu Bo Dong Jiwei Qin Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang 550025 Guizhou PR China State Key Laboratory of Public Big Data Guizhou university Guiyang 550025 Guizhou PR China. Electronic address: gs.cwyang21@***. State Key Laboratory of Public Big Data Guizhou university Guiyang 550025 Guizhou PR China. Electronic address: ypench@***. State Key Laboratory of Public Big Data Guizhou university Guiyang 550025 Guizhou PR China. Electronic address: gs.syu21@***. Department of Computer Science and Technology Xi'an Jiaotong University Xi'an 710049 Shanxi PR China. Electronic address: dong.bo@***. College of Information Science and Engineering Xinjiang University Urumqi 830046 Xinjiang PR China. Electronic address: jwqin_xju@***.

Mapping a sentence into a two-dimensional representation has the advantage of unfolding nested semantic structures in a sentence and encoding the interaction between tokens. In the planarized sentence representation, neighboring elements denote overlapped linguistic units in a sentence. An important phenomenon is that the semantic information of a true linguistic unit may penetrate neighboring elements, which blurs the semantic edge of a linguistic unit and disturbs the planarized sentence representation. Therefore, sharpening the semantic gradient helps aggravate semantic information from neighborhoods and depressing noises in neighboring elements. This paper reveals the mechanism of sharpening semantic gradient in the planarized sentence representation. Our method is evaluated on six evaluation datasets. The results show impressive improvement on three information extraction tasks. The success indicates that representing and processing sentences in a two-dimensional representation has a great potential to decode the sentential semantic structure and support sentence-level information extraction. Our code to implement the model is available at: https://***/caiwyang/Semantic_Gradient.

关键词： Information extraction Planarized sentence representation Semantic gradient

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Measurement and interpretation of same-sign W boson pair production in association with two jets in pp collisions at (Formula presented.) TeV with the ATLAS detector

引用

Journal of high Energy Physics 2024年第4期2024卷 1-61页

作者： Aad, G. Abbott, B. Abeling, K. Abicht, N.J. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Abusleme Hoffman, A.C. Acharya, B.S. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Addepalli, S.V. Addison, M.J. Adelman, J. Adiguzel, A. Adye, T. Affolder, A.A. Afik, Y. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ahuja, S. Ai, X. Aielli, G. Aikot, A. Ait Tamlihat, M. Aitbenchikh, B. Aizenberg, I. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Akiyama, D. Akolkar, N.N. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Albouy, G.L. Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, F. Algren, M. Alhroob, M. Ali, B. Ali, H.M.J. Ali, S. Alibocus, S.W. Aliev, M. Alimonti, G. Alkakhi, W. Allaire, C. Allbrooke, B.M.M. Allen, J.F. Allendes Flores, C.A. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alvarez Estevez, M. Alvarez Fernandez, A. Alves Cardoso, M. Alviggi, M.G. Aly, M. Amaral Coutinho, Y. Ambler, A. Amelung, C. Amerl, M. Ames, C.G. Amidei, D. Amor Dos Santos, S.P. Amos, K.R. Ananiev, V. Anastopoulos, C. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Anulli, F. Aoki, M. Aoki, T. Aparisi Pozo, J.A. Aparo, M.A. Aperio Bella, L. Appelt, C. Apyan, A. Aranzabal, N. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Arnaez, O. Arnold, H. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Assamagan, K. Astalos, R. Atashi, S. Atkin, R.J. Atkinson, M. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Auriol, A.D. Austrup, V.A. Avolio, G. Axiotis, K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahmani, M. Bailey, A.J. Bailey, V.R. Baines, J.T. Baines, L. Bakalis, C. Baker, O.K. Bakos, E. Bakshi Gupta, D. Balakrishnan, V. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Balli, F. Baltes, L.M. Balunas, W.K. Balz, J. Banas, E. Bandieramont Department of Physics University of Adelaide Department of Physics University of Alberta Department of Physics Ankara University Division of Physics TOBB University of Economics and Technology LAPP Université Savoie Mont Blanc CNRS/IN2P3 APC Université Paris Cité CNRS/IN2P3 High Energy Physics Division Argonne National Laboratory Department of Physics University of Arizona Department of Physics University of Texas at Arlington Physics Department National and Kapodistrian University of Athens Physics Department National Technical University of Athens Department of Physics University of Texas at Austin Institute of Physics Azerbaijan Academy of Sciences Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Institute of High Energy Physics Chinese Academy of Sciences Physics Department Tsinghua University Department of Physics Nanjing University School of Science Shenzhen Campus of Sun Yat-sen University University of Chinese Academy of Science (UCAS) Institute of Physics University of Belgrade Department for Physics and Technology University of Bergen Physics Division Lawrence Berkeley National Laboratory University of California Institut für Physik Humboldt Universität zu Berlin Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern School of Physics and Astronomy University of Birmingham Department of Physics Bogazici University Department of Physics Engineering Gaziantep University Department of Physics Istanbul University Facultad de Ciencias y Centro de Investigaciónes Universidad Antonio Nariño Departamento de Física Universidad Nacional de Colombia Dipartimento di Fisica e Astronomia A. Righi Università di Bologna INFN Sezione di Bologna Physikalisches Institut Universität Bonn Department of Physics Boston University Department of Physics Brandeis University Transilvania University of Brasov Horia Hulubei National Institute of Physics and Nuclear Engineering Department of Physics Alexandru

This paper presents the measurement of fiducial and differential cross sections for both the inclusive and electroweak production of a same-sign W-boson pair in association with two jets (W^±W^±jj) using 139 fb⁻¹ of proton-proton collision data recorded at a centre-of-mass energy of s = 13 TeV by the ATLAS detector at the Large Hadron Collider. The analysis is performed by selecting two same-charge leptons, electron or muon, and at least two jets with large invariant mass and a large rapidity difference. The measured fiducial cross sections for electroweak and inclusive W^±W^±jj production are 2.92 ± 0.22 (stat.) ± 0.19 (syst.) fb and 3.38 ± 0.22 (stat.) ± 0.19 (syst.) fb, respectively, in agreement with Standard Model predictions. The measurements are used to constrain anomalous quartic gauge couplings by extracting 95% confidence level intervals on dimension-8 operators. A search for doubly charged Higgs bosons H^±± that are produced in vector-boson fusion processes and decay into a same-sign W boson pair is performed. The largest deviation from the Standard Model occurs for an H^±± mass near 450 GeV, with a global significance of 2.5 standard deviations. © The Author(s) 2024.

关键词： Hadron-Hadron Scattering Vector Boson Production

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A Pipelining Loop Optimization Method for Dataflow Architecture

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Journal of computer science & technology 2018年第1期33卷 116-130页

作者： Xu Tan Xiao-Chun Ye Xiao-Wei Shen Yuan-Chao Xu Da Wang Lunkai Zhang Wen-Ming Li Dong-Rui Fan Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 1000J9 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China College of Information Engineering Capital Normal University Beijing 100048 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

With the coming of exascale supercomputing era, power efficiency has become the most important obstacle to build an exascale system. Dataflow architecture has native advantage in achieving high power efficiency for scientific applications. However, the state-of-the-art dataflow architectures fail to exploit high parallelism for loop processing. To address this issue, we propose a pipelining loop optimization method （PLO）, which makes iterations in loops flow in the processing element （PE） array of dataflow accelerator. This method consists of two techniques, architecture-assisted hardware iteration and instruction-assisted software iteration. In hardware iteration execution model, an on-chip loop controller is designed to generate loop indexes, reducing the complexity of computing kernel and laying a good f（mndation for pipelining execution. In software iteration execution model, additional loop instructions are presented to solve the iteration dependency problem. Via these two techniques, the average number of instructions ready to execute per cycle is increased to keep floating-point unit busy. Simulation results show that our proposed method outperforms static and dynamic loop execution model in floating-point efficiency by 2.45x and 1.1x on average, respectively, while the hardware cost of these two techniques is acceptable.

关键词： dataflow model control-flow model loop optimization exascale computing scientific application

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First observation of Λb0→Σc(*)++D(*)−K− decays

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Physical Review D 2024年第3期110卷 L031104页

作者： R. Aaij A. S. W. Abdelmotteleb C. Abellan Beteta F. Abudinén T. Ackernley A. A. Adefisoye B. Adeva M. Adinolfi P. Adlarson C. Agapopoulou C. A. Aidala Z. Ajaltouni S. Akar K. Akiba P. Albicocco J. Albrecht F. Alessio M. Alexander Z. Aliouche P. Alvarez Cartelle R. Amalric S. Amato J. L. Amey Y. Amhis L. An L. Anderlini M. Andersson A. Andreianov P. Andreola M. Andreotti D. Andreou A. Anelli D. Ao F. Archilli M. Argenton S. Arguedas Cuendis A. Artamonov M. Artuso E. Aslanides M. Atzeni B. Audurier D. Bacher I. Bachiller Perea S. Bachmann M. Bachmayer J. J. Back P. Baladron Rodriguez V. Balagura W. Baldini J. Baptista de Souza Leite M. Barbetti I. R. Barbosa R. J. Barlow S. Barsuk W. Barter M. Bartolini J. Bartz F. Baryshnikov J. M. Basels G. Bassi B. Batsukh A. Battig A. Bay A. Beck M. Becker F. Bedeschi I. B. Bediaga A. Beiter S. Belin V. Bellee K. Belous I. Belov I. Belyaev G. Benane G. Bencivenni E. Ben-Haim A. Berezhnoy R. Bernet S. Bernet Andres C. Bertella A. Bertolin C. Betancourt F. Betti J. Bex Ia. Bezshyiko J. Bhom M. S. Bieker N. V. Biesuz P. Billoir A. Biolchini M. Birch F. C. R. Bishop A. Bitadze A. Bizzeti T. Blake F. Blanc J. E. Blank S. Blusk V. Bocharnikov J. A. Boelhauve O. Boente Garcia T. Boettcher A. Bohare A. Boldyrev C. S. Bolognani R. Bolzonella N. Bondar F. Borgato S. Borghi M. Borsato J. T. Borsuk S. A. Bouchiba T. J. V. Bowcock A. Boyer C. Bozzi M. J. Bradley A. Brea Rodriguez N. Breer J. Brodzicka A. Brossa Gonzalo J. Brown D. Brundu E. Buchanan A. Buonaura L. Buonincontri A. T. Burke C. Burr A. Bursche A. Butkevich J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese L. Calefice S. Cali M. Calvi M. Calvo Gomez J. I. Cambon Bouzas P. Campana D. H. Campora Perez A. F. Campoverde Quezada S. Capelli L. Capriotti R. Caravaca-Mora A. Carbone L. Carcedo Salgado R. Cardinale A. Cardini P. Carniti L. Carus A. Casais Vidal R. Caspary G. Casse J. Castro Godinez M. Cattaneo G. Cavallero V. Cavallini S. Celani J. Cerasoli D. Cervenkov S. Cesare A. J. Chadwick I. Chahrour M. Charles Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Department of Physics University of Warwick Coventry United Kingdom Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Syracuse University Syracuse New York USA Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H. H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Department of Physics and Astronomy Uppsala University Uppsala Sweden (associated with School of Physics and Astronomy University of Glasgow Glasgow United Kingdom) European Organization for Nuclear Research (CERN) Geneva Switzerland University of Michigan Ann Arbor Michigan USA (associated with Syracuse University Syracuse New York USA) Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France University of Cincinnati Cincinnati Ohio USA INFN Laboratori Nazionali di Frascati Frascati Italy Fakultät Physik Technische Universität Dortmund Dortmund Germany School of Physics and Astronomy University of Glasgow Glasgow United Kingdom Department of Physics and Astronomy University of Manchester Manchester United Kingdom Cavendish Laboratory University of Cambridge Cambridge United Kingdom LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China INFN Sezione di Firenze Firenze Italy Affiliated with an institute covered by a cooperation agreement with CERN INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Milano-Bicocca Milano Italy University of Chinese Academy of Sciences Beijing China INFN Sezione di Roma Tor Vergata Roma Italy Consejo Nacional de Rectores (CONARE) San Jose Costa

The four decays, Λb0→Σc(*)++D(*)−K−, are observed for the first time using proton-proton collision data collected with the LHCb detector at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6 fb−1. By considering the Λb0→Λc+D¯0K− decay as reference channel, the following branching fraction ratios are measured to be B(Λb0→Σc++D−K−)B(Λb0→Λc+D¯0K−)=0.282±0.016±0.016±0.005, B(Λb0→Σc*++D−K−)B(Λb0→Σc++D−K−)=0.460±0.052±0.028, B(Λb0→Σc++D*−K−)B(Λb0→Σc++D−K−)=2.261±0.202±0.129±0.046, B(Λb0→Σc*++D*−K−)B(Λb0→Σc++D−K−)=0.896±0.137±0.066±0.018, where the first uncertainties are statistical, the second are systematic, and the third are due to uncertainties in the branching fractions of intermediate particle decays. These initial observations mark the beginning of pentaquark searches in these modes, with more datasets to become available following the LHCb upgrade.

关键词： Multiquark bound states Quantum chromodynamics Strong interaction

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A new metric improving Bayesian calibration of a multistage approach studying hadron and inclusive jet suppression

arXiv

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arXiv 2023年

作者： Fan, W. Vujanovic, G. Bass, S.A. Angerami, A. Arora, R. Cao, S. Chen, Y. Dai, T. Du, L. Ehlers, R. Elfner, H. Fries, R.J. Gale, C. He, Y. Heffernan, M. Heinz, U. Jacak, B.V. Jacobs, P.M. Jeon, S. Ji, Y. Kasper, L. Kordell, M. Kumar, A. Latessa, J. Lee, Y.-J. Lemmon, R. Liyanage, D. Lopez, A. Luzum, M. Majumder, A. Mak, S. Mankolli, A. Martin, C. Mehryar, H. Mengel, T. Mulligan, J. Nattrass, C. Norman, J. Paquet, J.-F. Parker, C. Putschke, J.H. Roland, G. Schenke, B. Schwiebert, L. Sengupta, A. Shen, C. Sirimanna, C. Soeder, D. Soltz, R.A. Soudi, I. Strickland, M. Tachibana, Y. Velkovska, J. Wang, X.-N. Zhao, W. Department of Physics Duke University DurhamNC27708 United States Department of Physics and Astronomy Wayne State University DetroitMI48201 United States Department of Physics University of Regina ReginaSKS4S 0A2 Canada Lawrence Livermore National Laboratory LivermoreCA94550 United States Research Computing Group University Technology Solutions The University of Texas at San Antonio San AntonioTX78249 United States Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Laboratory for Nuclear Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics McGill University MontréalQCH3A2T8 Canada Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Physics Division Oak Ridge National Laboratory Oak RidgeTN37830 United States GSI Helmholtzzentrum für Schwerionenforschung Darmstadt64291 Germany Institute for Theoretical Physics Goethe University Frankfurt am Main60438 Germany Frankfurt Institute for Advanced Studies Frankfurt am Main60438 Germany Cyclotron Institute Texas A&M University College StationTX77843 United States Department of Physics and Astronomy Texas A&M University College StationTX77843 United States Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou510006 China Department of Physics The Ohio State University ColumbusOH43210 United States Department of Physics University of California BerkeleyCA94270 United States Nuclear Science Division Lawrence Berkeley National Laboratory BerkeleyCA94270 United States Department of Statistical Science Duke University DurhamNC27708 United

We study parton energy-momentum exchange with the quark gluon plasma (QGP) within a multistage approach composed of in-medium DGLAP evolution at high virtuality, and (linearized) Boltzmann Transport formalism at lower virtuality. This multistage simulation is then calibrated in comparison with high pT charged hadrons, D-mesons, and the inclusive jet nuclear modification factors, using Bayesian model-to-data comparison, to extract the virtuality-dependent transverse momentum broadening transport coefficient qˆ. To facilitate this undertaking, we develop a quantitative metric for validating the Bayesian workflow, which is used to analyze the sensitivity of various model parameters to individual observables. The usefulness of this new metric in improving Bayesian model emulation is shown to be highly beneficial for future such analyses. © 2023, CC BY.

关键词： Hadrons

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FrepJoin:an efficient partition-based algorithm for edit similarity join

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Frontiers of Information technology & Electronic Engineering 2017年第10期18卷 1499-1510页

作者： Ji-zhou LUO Sheng-fei SHI Hong-zhi WANG Jian-zhong LI School of Computer Science and Technology Harbin Institute of Technology Guangdong Key Laboratory of Popular High Performance Computers Key Laboratory of Service Computing and Application

String similarity join(SSJ) is essential for many applications where near-duplicate objects need to be found. This paper targets SSJ with edit distance constraints. The existing algorithms usually adopt the filter-andrefine framework. They cannot catch the dissimilarity between string subsets, and do not fully exploit the statistics such as the frequencies of characters. We investigate to develop a partition-based algorithm by using such *** frequency vectors are used to partition datasets into data chunks with dissimilarity between them being caught easily. A novel algorithm is designed to accelerate SSJ via the partitioned data. A new filter is proposed to leverage the statistics to avoid computing edit distances for a noticeable proportion of candidate pairs which survive the existing filters. Our algorithm outperforms alternative methods notably on real datasets.

关键词： String similarity join Edit distance Filter and refine Data partition Combined frequency vectors

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