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, 006 (2022) ·
published 11 July 2022
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In the standard model, fermions acquire the mass via the Yukawa interaction. This mechanism can be tested by measuring couplings of the fermions with the Higgs boson. At the ATLAS experiment, the Higgs-fermion coupling measurements became possible thanks to the abundant dataset: the integrated luminosity of 139~fb$^{-1}$ at the centre-of-mass energy of 13~TeV as well as 25~fb$^{-1}$ at 7--8~TeV. This paper presents recent results from measurements of the Higgs boson productions in decays to a fermion pair in the final state with the ATLAS detector.
