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, 117 (2022) ·
published 13 July 2022
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The large dataset of about 3000 $\rm{fb}^{-1}$ that will be collected at the High Luminosity LHC (HL-LHC) will be used to measure Higgs boson processes in detail. This large dataset will also provide sensitivity to di-Higgs processes and will allow for the improvement of the constraints on the Higgs boson self coupling. Studies based on current ATLAS analyses using LHC Run 2 data have been carried out to understand the expected precision and limitations of the Higgs and di-Higgs measurements at the HL-LHC. This paper presents the ATLAS prospects for Higgs and di-Higgs results at the HL-LHC.
