SciPost Phys. 19, 155 (2025) ·
published 16 December 2025
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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.
SciPost Phys. Proc. 8, 001 (2022) ·
published 11 July 2022
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Many of the Higgs boson production and decay channels, predicted by the StandardModel, have been observed since its 2012 discovery. Today, measurements of the Higgsboson’s properties in these channels deepen our understanding of the nature of thisfundamental particle by providing better information about its mass, production kine-matics, and CP structure. Increasing precision also enables targeting rare Higgs bosondecay modes and probing di-Higgs-boson production. In addition, searches for beyondStandard Model physics in the Higgs sector are performed. This article presents the lat-est Higgs boson measurement highlights from the ATLAS and CMS collaborations usingLHC proton-proton collision data at a centre of mass energy of 13 TeV.
