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

• Published in: European Physical Journal C Vol. 83; no. 7; p. 681
• Main Authors: Abusleme Hoffman, A. C., Angerami, A., Armbruster, A. J., Assamagan, K., Balaji, S., Balek, P., Battulga, D., Bayirli, A., Berthold, A., Bielski, R., Bird, G. A., Bisanz, T., Booth, C. D., Borgna, L. S., Buzykaev, A. R., Caforio, D., Calvet, T. P., Castro, N. F., Cerri, A., Chargeishvili, B., Chu, X., Costanza, F., Cuhadar Donszelmann, T., Gao, J., García, C., Gasiorowski, S. J., Gazis, E. N., Grenier, P., Gubbels, C., Guo, L., Haug, S., Hawkes, C. M., Hays, J. M., Heinrich, J. J., Helsens, C., Huang, Z., Iconomidou-Fayard, L., Jacobs, R. M., Kabana, S., Kelsey, D., Kersten, S., Ketabchi Haghighat, S., Kopeliansky, R., Kramer, P., Levêque, J., Lindon, J. H., Lorenz, J., Love, P. A., Lozano Bahilo, J. J., Lu, Y. J., Meyer, C., Mikuž, M., Miller, D. W., Mitra, A., Miyagawa, P. S., Moreira De Carvalho, A. L., Moser, B., Nikiforou, N., Nisati, A., Norman, B. J., Olszowska, J., Orestano, D., Palestini, S., Panchal, D. K., Panizzo, G., Readioff, N. P., Rodriguez Bosca, S., Rodríguez Vera, A. M., Ruelas Rivera, V. H., Rühr, F., Sankey, D. P. C., Santos, H., Schenck, F., Sen, S., Shirabe, S., Sijacki, Dj, Skaf, A., Smiesko, J., Soffer, A., Stapf, B., Stark, J., Steentoft, J., Tassi, E., Thomas, D. W., Torres, H., Tuna, A. N., Ukegawa, F., Veneziano, S., Vickey, T., Von Ahnen, J., Vos, M., Vreeswijk, M., Wagner, W., Wang, A. Z., Wang, S., Winter, J. K., Xu, Z., Yang, H. T., Yeh, Y., Zhang, J.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2023; Springer; Springer Nature B.V; Springer Verlag (Germany); Springer Nature
• Subjects: Algorithms; Artificial neural networks; Astronomy; Astrophysics and Cosmology; Collaboration; Computer simulation; Data Analysis, Statistics and Probability; Datasets; Efficiency; Elementary Particles; Flavor (particle physics); Fysik; Hadrons; Heavy Ions; High Energy Physics - Experiment; 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; ATLAS; bottom particle: particle identification; data analysis method; efficiency; numerical calculations; performance; charmed particle: particle identification; p p: scattering; neural network
• ISSN: 1434-6044; 1434-6052; 1431-5858; 1434-6052
• DOI: 10.1140/epjc/s10052-023-11699-1

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.