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, 157 (2022) ·
published 14 July 2022
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The ATLAS experiment at the Large Hadron Collider (LHC) measures proton--proton collisions at high energies. Within the Phase-I upgrade of the LHC before the start of Run-3 in 2022, the trigger system of the Liquid Argon calorimeter of ATLAS is being prepared to cope with an increased number of simultaneous proton--proton collisions. In the back-end of this new trigger system, the LATOME boards will be responsible for the computation of the energies deposited in the calorimeter. The commissioning of this computation within the LATOME is presented.
