Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network

被引:31
|
作者
Aad, G. [157 ]
Aakvaag, E. [19 ]
Abbott, B. [178 ]
Abeling, K. [81 ]
Abicht, N. J. [74 ]
Abidi, S. H. [45 ]
Aboulhorma, A. [58 ,243 ]
Abramowicz, H. [222 ]
Abreu, H. [221 ]
Abulaiti, Y. [175 ]
Acharya, B. S. [105 ,106 ,256 ]
Bourdarios, C. Adam [4 ]
Adamczyk, L. [136 ]
Addepalli, S. V. [36 ]
Addison, M. J. [156 ]
Adelman, J. [172 ]
Adiguzel, A. [28 ]
Adye, T. [197 ]
Affolder, A. A. [199 ]
Afik, Y. [61 ]
Agaras, M. N. [12 ]
Agarwala, J. [114 ,115 ]
Aggarwal, A. [155 ]
Agheorghiesei, C. [39 ]
Ahmad, A. [60 ]
Ahmadov, F. [60 ,269 ]
Ahmed, W. S. [159 ]
Ahuja, S. [149 ]
Ai, X. [95 ]
Aielli, G. [120 ,121 ]
Aikot, A. [236 ]
Tamlihat, M. Ait [58 ]
Aitbenchikh, B. [55 ]
Aizenberg, I. [242 ]
Akbiyik, M. [155 ]
Akesson, T. P. A. [152 ]
Akimov, A. V. [60 ]
Akiyama, D. [241 ]
Akolkar, N. N. [34 ]
Aktas, S. [26 ]
Al Khoury, K. [63 ]
Alberghi, G. L. [33 ]
Albert, J. [238 ]
Albicocco, P. [78 ,79 ]
Albouy, G. L. [87 ]
Alderweireldt, S. [77 ]
Alegria, Z. L. [179 ]
Aleksa, M. [60 ]
Aleksandrov, I. N. [60 ]
Alexa, C. [38 ]
机构
[1] Univ Adelaide, Dept Phys, Adelaide, SA, Australia
[2] Univ Alberta, Dept Phys, Edmonton, AB, Canada
[3] Ankara Univ, Dept Phys, Ankara, Turkiye
[4] Univ Savoie Mont Blanc, LAPP, CNRS, IN2P3, Annecy, France
[5] Argonne Natl Lab, Div High Energy Phys, Argonne, IL USA
[6] Univ Arizona, Dept Phys, Tucson, AZ USA
[7] Univ Texas Arlington, Dept Phys, Arlington, TX USA
[8] Natl & Kapodistrian Univ Athens, Dept Phys, Athens, Greece
[9] Natl Tech Univ Athens, Dept Phys, Zografos, Greece
[10] Univ Texas Austin, Dept Phys, Austin, TX USA
[11] Azerbaijan Acad Sci, Inst Phys, Baku, Azerbaijan
[12] Barcelona Inst Sci & Technol, Inst Fis Altes Energies IFAE, Barcelona, Spain
[13] Chinese Acad Sci, Inst High Energy Phys, Beijing, Peoples R China
[14] Tsinghua Univ, Dept Phys, Beijing, Peoples R China
[15] Nanjing Univ, Dept Phys, Nanjing, Peoples R China
[16] Sun Yat Sen Univ, Sch Sci, Shenzhen Campus, Shenzhen, Guangdong, Peoples R China
[17] Univ Chinese Acad Sci UCAS, Beijing, Peoples R China
[18] Univ Belgrade, Inst Phys, Belgrade, Serbia
[19] Univ Bergen, Dept Phys & Technol, Bergen, Norway
[20] Lawrence Berkeley Natl Lab, Div Phys, Berkeley, CA USA
[21] Univ Calif Berkeley, Berkeley, CA USA
[22] Humboldt Univ, Inst Phys, Berlin, Germany
[23] Univ Bern, Albert Einstein Ctr Fundamental Phys, Bern, Switzerland
[24] Univ Bern, High Energy Phys Lab, Bern, Switzerland
[25] Univ Birmingham, Sch Phys & Astron, Birmingham, W Midlands, England
[26] Bogazici Univ, Dept Phys, Istanbul, Turkiye
[27] Gaziantep Univ, Dept Engn Phys, Gaziantep, Turkiye
[28] Istanbul Univ, Dept Phys, Istanbul, Turkiye
[29] Univ Antonio Narino, Fac Ciencias, Bogota, Colombia
[30] Univ Antonio Narino, Ctr Invest, Bogota, Colombia
[31] Univ Nacl Colombia, Dept Fis, Bogota, Colombia
[32] Univ Bologna, Dipartimento Fis & Astron A Righi, Bologna, Italy
[33] INFN, Sez Bologna, Bologna, Italy
[34] Univ Bonn, Phys Inst, Bonn, Germany
[35] Boston Univ, Dept Phys, Boston, MA USA
[36] Brandeis Univ, Dept Phys, Waltham, MA USA
[37] Transilvania Univ Brasov, Brasov, Romania
[38] Horia Hulubei Natl Inst Phys & Nucl Engn, Bucharest, Romania
[39] Alexandru Ioan Cuza Univ, Dept Phys, Iasi, Romania
[40] Natl Inst Res & Dev Isotop & Mol Technol, Dept Phys, Cluj Napoca, Romania
[41] Natl Univ Sci & Technol Politech, Bucharest, Romania
[42] West Univ Timisoara, Timisoara, Romania
[43] Comenius Univ, Fac Math, Phys & Informat, Bratislava, Slovakia
[44] Slovak Acad Sci, Inst Expt Phys, Dept Subnuclear Phys, Kosice, Slovakia
[45] Brookhaven Natl Lab, Dept Phys, Upton, NY USA
[46] Univ Buenos Aires, Fac Ciencias Exactas & Nat, Dept Fis, Buenos Aires, DF, Argentina
[47] Consejo Nacl Invest Cient & Tecn, Inst Fis Buenos Aires IFIBA, Buenos Aires, DF, Argentina
[48] Calif State Univ, San Pablo, CA USA
[49] Univ Cambridge, Cavendish Lab, Cambridge, England
[50] Univ Cape Town, Dept Phys, Cape Town, South Africa
来源
MACHINE LEARNING-SCIENCE AND TECHNOLOGY | 2024年 / 5卷 / 03期
基金
日本学术振兴会; 中国国家自然科学基金; 欧洲研究理事会; 瑞典研究理事会; 加拿大自然科学与工程研究理事会; 巴西圣保罗研究基金会; 瑞士国家科学基金会;
关键词
ATLAS; detector; CERN jets; calibrations;
D O I
10.1088/2632-2153/ad611e
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The energy and mass measurements of jets are crucial tasks for the Large Hadron Collider experiments. This paper presents a new calibration method to simultaneously calibrate these quantities for large-radius jets measured with the ATLAS detector using a deep neural network (DNN). To address the specificities of the calibration problem, special loss functions and training procedures are employed, and a complex network architecture, which includes feature annotation and residual connection layers, is used. The DNN-based calibration is compared to the standard numerical approach in an extensive series of tests. The DNN approach is found to perform significantly better in almost all of the tests and over most of the relevant kinematic phase space. In particular, it consistently improves the energy and mass resolutions, with a 30% better energy resolution obtained for transverse momenta p(T) > 500 GeV.
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页数:37
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