PT Journal
AU ATLAS Collaboration (Aad, Gea
   Amos, KR
   Aparisi Pozo, JA
   Bailey, AJ
   Bouchhar, N
   Cabrera Urban, S
   Cantero, J
   Cardillo, F
   Castillo Gimenez, V
   Chitishvili, M
   Costa, MJ
   Didenko
   Escobar, C
   Fiorini, L
   Fullana Torregrosa, E
   Fuster, J
   Garcia, C
   Garcia Navarro, JE
   Gomez Delegido, AJ
   Gonzalez de la Hoz, S
   Gonzalvo Rodriguez, GR
   Guerrero Rojas, JGR
   Lacasta, C
   Lozano Bahilo, JJ
   Marti-Garcia, S
   Martinez Agullo, P
   Miralles Lopez, M
   Mitsou, VA
   Monsonis Romero, L
   Moreno Llacer, M
   Munoz Perez, D
   Navarro-Gonzalez, J
   Poveda, J
   Prades Ibañez, A
   Rubio Jimenez, A
   Ruiz-Martinez, A
   Sabatini, P
   Salt, J
   Sanchez Sebastian, V
   Sayago Galvan, I
   Senthilkumar, V
   Soldevila, U
   Sanchez, J
   Torro Pastor, E
   Valero, A
   Valls Ferrer, JA
   Varriale, L
   Villaplana Perez, M
   Vos, M
TI ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
SO European Physical Journal C
JI Eur. Phys. J. C
PY 2023
BP 681 - 37pp
VL 83
IS 7
DI 10.1140/epjc/s10052-023-11699-1
LA English
AB The flavour-tagging algorithms developed by the AvTLAS Collaboration and used to analyse its dataset of root 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) over bar events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.
ER