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. 10, 012 (2022) ·
published 10 August 2022
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The production of $W$/$Z$-bosons in association with jets is an important test of perturbative QCD predictions and also yields information about the parton distribution functions of the proton. We present fits to determine PDFs using inclusive $W$/$Z$-boson and $W$/$Z$+jets measurements from the ATLAS experiment at the LHC. The ATLAS measurements are used in combination with deep-inelastic scattering data from HERA. An improved determination of the sea-quark densities at high Bjorken-$x$ is seen, while confirming a strange-quark density similar in size to the up- and down-sea-quark densities in the range $x<0.02$ found by previous ATLAS analyses.
