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

ATLAS Collaboration

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

Sammanfattning

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 tt¯ events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained. © 2023, The Author(s).
Originalspråkengelska
Artikelnummer681
TidskriftEuropean Physical Journal C
Volym83
Nummer7
DOI
StatusPublished - 2023

Ämnesklassifikation (UKÄ)

  • Subatomär fysik

Fingeravtryck

Utforska forskningsämnen för ”ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här