ATLAS flavour-tagging algorithms for the LHC Run 2 <i>pp</i> collision dataset



Aad, G, Abbott, B, Abeling, K, Abicht, NJ, Abidi, SH, Aboulhorma, A, Abramowicz, H, Abreu, H, Abulaiti, Y, Abusleme Hoffman, AC
et al (show 2922 more authors) (2023) ATLAS flavour-tagging algorithms for the LHC Run 2 <i>pp</i> collision dataset. EUROPEAN PHYSICAL JOURNAL C, 83 (7). 681-.

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Abstract

<jats:title>Abstract</jats:title><jats:p>The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of <jats:inline-formula><jats:alternatives><jats:tex-math>$$\sqrt{s} = 13$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> <mml:mo>=</mml:mo> <mml:mn>13</mml:mn> </mml:mrow> </mml:math></jats:alternatives></jats:inline-formula> TeV <jats:italic>pp</jats:italic> 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% <jats:italic>b</jats:italic>-jet identification efficiency operating point, light-jet (charm-jet) rejection factors of 170 (5) are achieved in a sample of simulated Standard Model <jats:inline-formula><jats:alternatives><jats:tex-math>$$t\bar{t}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>t</mml:mi> <mml:mover> <mml:mrow> <mml:mi>t</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>¯</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> </mml:math></jats:alternatives></jats:inline-formula> events; similarly, at a <jats:italic>c</jats:italic>-jet identification efficiency of 30%, a light-jet (<jats:italic>b</jats:italic>-jet) rejection factor of 70 (9) is obtained.</jats:p>

Item Type: Article
Uncontrolled Keywords: 5106 Nuclear and Plasma Physics, 5107 Particle and High Energy Physics, 51 Physical Sciences
Divisions: Faculty of Science and Engineering > School of Physical Sciences
Depositing User: Symplectic Admin
Date Deposited: 09 Aug 2023 10:26
Last Modified: 22 Jun 2024 17:36
DOI: 10.1140/epjc/s10052-023-11699-1
Open Access URL: https://link.springer.com/article/10.1140/epjc/s10...
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3172094