SciPost Phys. 19, 155 (2025) ·
published 16 December 2025
|
· pdf
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. 16, 020 (2025) ·
published 15 July 2025
|
· pdf
The appearance of lepton-flavour-violating processes in LHC proton-proton collisions is one of the possible ways that new physics beyond the Standard Model could manifest itself. This proceeding summarizes the most recent searches for lepton-flavour-violating processes and tests of lepton flavour universality with the ATLAS and CMS detectors, using proton-proton collisions at a centre-of-mass energy of 13 TeV.
