Speaker
Description
Identifying the flavour of hadronic jets is a cornerstone of the ATLAS physics programme, underpinning measurements of the Higgs boson and top quark as well as searches for di-Higgs production and physics beyond the Standard Model. We present the latest generation of ATLAS flavour-tagging algorithms, which have evolved from hybrid multi-stage approaches into unified transformer architectures trained end-to-end on low-level detector information.
The current-generation tagger, GN2, recently published in Nature Communications, improves the rejection of $c$-jets (light-flavour jets) by a factor of 3.5 (1.8) with respect to its predecessor at a 70% $b$-jet efficiency. Its successor, GN3, pushes this paradigm further: a multi-modal transformer combining charged-particle tracks, neutral particle-flow objects, and soft-lepton information via a learned object-association scheme, trained with physics-informed auxiliary objectives such as track-origin classification and jet-$p_\mathrm{T}$ regression. GN3 extends the classification space to strange-quark, up/down-quark, and gluon jets, and delivers a further 5.2% (6.1%) absolute gain in $b$-jet ($c$-jet) efficiency at fixed background rejection. The multi-task design also enables qualitatively new capabilities: an auxiliary charge-prediction objective yields the first transformer-based ATLAS tagger to separate $b$- from $\bar{b}$-initiated and $c$- from $\bar{c}$-initiated jets.
We discuss the architecture, training workflow, the newest performance results and give an outlook on new developments.