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, 031 (2022) ·
published 11 July 2022
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Precision measurements of the production cross-sections of W and Z boson at LHC provide important tests of perturbative QCD and information about the parton distribution functions for quarks within the proton. We present measurements of cross sections for inclusive $W^+$, $W^{-}$ and Z boson production using data collected by the ATLAS experiment at a center-of-mass energy of $\sqrt{s}$=2.76 TeV. Measurement of the transverse momentum distribution of Z boson at $\sqrt{s}$=13 TeV is also presented. The measurements are corrected for detector effects and compared with state-of-the-art theoretical calculations.
