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

Mixed citation behavior. Most common role is method (60%).

12 Pith papers citing it
Method 60% of classified citations
abstract

The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of $\sqrt s = 13$ 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 $t\bar{t}$ events; similarly, at a $c$-jet identification efficiency of 30%, a light-jet ($b$-jet) rejection factor of 70 (9) is obtained.

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method 3 background 2

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representative citing papers

Track and Vertex Reconstruction with the ATLAS Inner Detector

physics.ins-det · 2026-05-08 · unverdicted · novelty 2.0

ATLAS Inner Detector track and vertex reconstruction maintains high efficiency, good resolution, and low fake rates for up to 80 simultaneous proton-proton interactions in Run 2 and Run 3 data and simulations.

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Showing 12 of 12 citing papers.