Karlsruhe 2024 – scientific programme

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T: Fachverband Teilchenphysik

T 20: Higgs 1 (boson final states)

T 20.2: Talk

Monday, March 4, 2024, 16:15–16:30, Geb. 30.41: HS 2

Machine-learning-based optimisation of Higgs coupling measurements in the H → 4l decay channel with ATLAS Run 3 data — •Luca Spitzauer, Sandra Kortner, Alice Reed, and Hubert Kroha — Max-Planck-Institut für Physik

Cross-section measurements for different Higgs boson production and decay processes constitute a key area in the exploration of Higgs properties, with a high sensitivity to potential physics beyond the Standard Model. Due to its exceptionally clear signal, the decay of a Higgs boson into a ZZ^* pair with a subsequent decay of each Z boson into two leptons, H → ZZ^* → 4l, is one of the most important channels for the Higgs property measurements.

Optimized classification of events according to these ’production bins’ is vital to improve the signal sensitivity and reduce sources of uncertainty. Previous round of STXS measurements in the H → 4l channel with the Run 2 ATLAS dataset employed a Neural Network classification approach. With the new Run 3 dataset at a centre-of-mass energy of 13.6 TeV, potential optimization of this classification is explored by means of additional machine-learning approaches with improved architectures.

Keywords: ATLAS; Higgs; Machine Learning; Cross-Section; Lepton