Transforming Boosted Jet Tagging with the ATLAS Detector: ML algorithms and their performance

14 Jul 2026, 09:00
20m

Speaker

Osama Karkout (Nikhef National institute for subatomic physics (NL))

Description

The identification of Higgs bosons with high transverse momenta is essential for the Higgs-boson research programme at the LHC, reaching across cross-section measurements in high transverse momentum regimes, Higgs-boson pair production searches, and searches for new heavy resonances. We present recent advances in Higgs-boson identification algorithms for cases where the Higgs-boson decay products are captured in a single large-radius jet using the ATLAS detector. These advances are driven by state-of-the-art machine learning techniques based on transformer architectures, providing up to a factor of 3 improvement compared to sub-jet tagging based approaches. The performance of these large-radius taggers for different Higgs-boson decay modes will be presented, discussing the rejection of large-radius jets that originated from top-quark decays, gluon splitting and W-boson decays. A brief outlook on the calibration strategies will be given as well.

Author

Osama Karkout (Nikhef National institute for subatomic physics (NL))

Presentation materials