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ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset

by Walkowiak, W. , Raine, J. A. , Cuhadar Donszelmann, T. , Lucotte, A. , Rendel, M. B. , Watson, H. , Introzzi, G. , Zanzi, D. , Pan, J. , Gao, Y. , Strom, D. M. , Li, C.-Q. , Yang, Z. , Ezhilov, A. , Fabbri, L. , Falke, P. J. , Emerman, A. , Aguilar-Saavedra, J. A. , Carminati, L. , Cantero, J. , Hsu, S.-C. , Pedro, R. , Carlotto, J. I. , Meyer, C. , Guerrieri, G. , Luise, I. , Mellado Garcia, B. R. , Zhang, P. , Diaz, M. A. , Dudarev, A. , Booth, C. D. , Atmani, H. , Borissov, G. , Kajomovitz, E. , Brüers, B. , Yang, Y. , Lyons, H. , Catmore, J. R. , Calafiura, P. , Garg, R. B. , Gonzalez-Sevilla, S. , Korn, A. , Shaheen, R. , Pogrebnyak, I. , Sauerburger, F. , Bhopatkar, V. S. , Simpson, E. L. , Hessey, N. P. , Schaefer, D. , Tnourji, A. , Trofymov, A. , Zhang, S. , Zhukov, K. , Golubkov, D. , Kang, N. J. , Krüger, H. , Kupfer, T. , Principe Martin, M. A. , Heidorn, W. D. , Berthold, A. , Hamdaoui, H. , Gazis, E. N. , Batley, J. R. , Vokac, P. , Hawkings, R. J. , Busch, E. L. , Melo, A. H. , Yang, X. , Kim, Y. K. , Levin, D. , Tu, Y. , Schwarz, T. A. , Ward, R. J. , Lehmann, K. , Calace, N. , Zoccoli, A. , Raskina, V. , Jia, J. , Viehhauser, G. H. A. , Pettee, M. , Cameron, D. , Tariq, K. , Huang, S. , Junkermann, T. , Veloso, F. , Zh

in Algorithms / Artificial neural networks / Astronomy / Astrophysics and Cosmology / Collaboration / Computer simulation / Datasets / Efficiency / Elementary Particles / Flavor (particle physics) / Fysik / Hadrons / Heavy Ions / Large Hadron Collider / Magnetic fields / Measurement Science and Instrumentation / Natural Sciences / Naturvetenskap / Neural networks / Nuclear Energy / Nuclear Physics / Physical Sciences / Physics / Physics and Astronomy / Quantum Field Theories / Quantum Field Theory / Quarks / Radiation / Regular Article - Experimental Physics / Rejection / Sensors / Silicon / Software / String Theory / Subatomic Physics / Subatomär fysik

2023

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ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset

2023

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ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset

2023

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Journal Article

ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset

Walkowiak, W.,

Raine, J. A.,

Cuhadar Donszelmann, T.,

Lucotte, A.,

Rendel, M. B.,

Watson, H.,

Introzzi, G.,

Zanzi, D.,

Pan, J.,

Gao, Y.,

Strom, D. M.,

Li, C.-Q.,

Yang, Z.,

Ezhilov, A.,

Fabbri, L.,

Falke, P. J.,

Emerman, A.,

Aguilar-Saavedra, J. A.,

Carminati, L.,

Cantero, J.,

Hsu, S.-C.,

Pedro, R.,

Carlotto, J. I.,

Meyer, C.,

Guerrieri, G.,

Luise, I.,

Mellado Garcia, B. R.,

Zhang, P.,

Diaz, M. A.,

Dudarev, A.,

Booth, C. D.,

Atmani, H.,

Borissov, G.,

Kajomovitz, E.,

Brüers, B.,

Yang, Y.,

Lyons, H.,

Catmore, J. R.,

Calafiura, P.,

Garg, R. B.,

Gonzalez-Sevilla, S.,

Korn, A.,

Shaheen, R.,

Pogrebnyak, I.,

Sauerburger, F.,

Bhopatkar, V. S.,

Simpson, E. L.,

Hessey, N. P.,

Schaefer, D.,

Tnourji, A.,

Trofymov, A.,

Zhang, S.,

Zhukov, K.,

Golubkov, D.,

Kang, N. J.,

Krüger, H.,

Kupfer, T.,

Principe Martin, M. A.,

Heidorn, W. D.,

Berthold, A.,

Hamdaoui, H.,

Gazis, E. N.,

Batley, J. R.,

Vokac, P.,

Hawkings, R. J.,

Busch, E. L.,

Melo, A. H.,

Yang, X.,

Kim, Y. K.,

Levin, D.,

Tu, Y.,

Schwarz, T. A.,

Ward, R. J.,

Lehmann, K.,

Calace, N.,

Zoccoli, A.,

Raskina, V.,

Jia, J.,

Viehhauser, G. H. A.,

Pettee, M.,

Cameron, D.,

Tariq, K.,

Huang, S.,

Junkermann, T.,

Veloso, F.,

2023

Overview

The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of s = 13 TeV pp collisions from Run 2 of the Large Hadron Collider are presented. These new tagging algorithms are based on recurrent and deep neural networks, and their performance is evaluated in simulated collision events. These developments yield considerable improvements over previous jet-flavour identification strategies. At the 77% b -jet identification efficiency operating point, light-jet (charm-jet) rejection factors of 170 (5) are achieved in a sample of simulated Standard Model t t ¯ events; similarly, at a c -jet identification efficiency of 30%, a light-jet ( b -jet) rejection factor of 70 (9) is obtained.

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Publisher

Springer Berlin Heidelberg,Springer,Springer Nature B.V,Springer Nature

Subject

Algorithms

/ Artificial neural networks

/ Astronomy

/ Astrophysics and Cosmology

/ Collaboration

/ Computer simulation

/ Datasets