Transforming jet flavour tagging at ATLAS
ATLAS Collaboration

TL;DR
This paper introduces GN2, a transformer-based jet flavour tagging algorithm for ATLAS that improves heavy-flavour jet identification by processing low-level tracking data with physics-informed training, validated in real collision data.
Contribution
The paper presents GN2, a novel transformer-based algorithm for jet flavour tagging that enhances interpretability and performance over previous methods in ATLAS.
Findings
C-jet rejection improved by a factor of 3.5 in data.
Light-jet rejection improved by a factor of 1.8 in data.
Validated performance in both simulation and collision data.
Abstract
Jet flavour tagging enables the identification of jets originating from heavy-flavour quarks in proton-proton collisions at the Large Hadron Collider, playing a critical role in its physics programmes. This paper presents GN2, a transformer-based flavour tagging algorithm deployed by the ATLAS Collaboration that represents a different methodology compared to previous approaches. Designed to classify jets based on the flavour of their constituent particles, GN2 processes low-level tracking information in an end-to-end architecture and incorporates physics-informed auxiliary training objectives to enhance both interpretability and performance. Its performance is validated in both simulation and collision data. The measured c-jet (light-jet) rejection in data is improved by a factor of 3.5 (1.8) for a 70% b-jet tagging efficiency, compared to the previous algorithm. GN2 provides…
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