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The flavour-tagging algorithms developed by the ATLAS Collaboration and usedto analyse its dataset of $\sqrt s = 13$ TeV $pp$ collisions from Run 2 of theLarge Hadron Collider are presented. These new tagging algorithms are based onrecurrent and deep neural networks, and their performance is evaluated insimulated collision events. These developments yield considerable improvementsover previous jet-flavour identification strategies. At the 77% $b$-jetidentification efficiency operating point, light-jet (charm-jet) rejectionfactors 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%, alight-jet ($b$-jet) rejection factor of 70 (9) is obtained.

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