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, 112 (2022) ·
published 13 July 2022
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Comprehensive measurements of differential cross-sections of top-quark-antiquark pair-production are presented. They are performed in the dilepton, lepton+jets and the all-hadronic channels, using $\sqrt{s}=13$ TeV data from the ATLAS detector at the LHC. Several setups of next-to-leading order and next-to-next-to-leading order generators are quantitatively compared to the measurements. In addition, total cross-section measurements using up to the full ATLAS Run 2 dataset are presented.
