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


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Aad G., Abbott B., Abbott D., Abeling K., Abidi S., Aboulhorma A., ...Daha Fazla

European Physical Journal C, cilt.83, sa.7, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 83 Sayı: 7
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1140/epjc/s10052-023-11699-1
  • Dergi Adı: European Physical Journal C
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Chemical Abstracts Core, Communication Abstracts, INSPEC, zbMATH, Directory of Open Access Journals
  • İstanbul Üniversitesi Adresli: Evet

Özet

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.