Contributor info: Mr Chris Scheulen

Chris Scheulen, on behalf of the ATLAS collaboration

SciPost Phys. Proc. 18, 011 (2026) · published 29 January 2026 | Toggle abstract · pdf

A measurement of Higgs boson production in association with a top quark pair in the bottom--anti-bottom Higgs boson decay channel and leptonic top final states is presented. The analysis uses $140\,\mathrm{fb}^{-1}$ of $13\,\mathrm{TeV}$ proton--proton collision data collected by the ATLAS detector at the Large Hadron Collider. A particular focus is placed on the role played by transformer neural networks in discriminating signal and background processes via multi-class discriminants and in reconstructing the Higgs boson transverse momentum. These powerful multi-variate analysis techniques significantly improve the analysis over a previous measurement using the same dataset. As a result, the observed (expected) event excess over the background-only hypothesis corresponds with a significance of 4.6 (5.4) standard deviations.

ATLAS Collaboration

SciPost Phys. 19, 155 (2025) · published 16 December 2025 | Toggle abstract · pdf

The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach not only yields a continuous and smooth calibration function that improves performance relative to the standard calibration but also provides uncertainties on the calibrated energies for each topo-cluster. The results obtained by using a trained BNN are compared to the standard local hadronic calibration and to a calibration provided by training a deep neural network. The uncertainties predicted by the BNN are interpreted in the context of a fractional contribution to the systematic uncertainties of the trained calibration. They are also compared to uncertainty predictions obtained from an alternative estimator employing repulsive ensembles.