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, 081 (2022) ·
published 12 July 2022
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We present results on the inclusive and identified (pion, kaon, proton and their antiparticles) charged-particle production in single diffractive (SD) dissociation process in proton-proton collisions at $\sqrt{s}=200$ GeV with the STAR detector at RHIC. The forward-scattered proton is measured in the Roman Pot (RP) system, while the charged particle tracks are reconstructed in the STAR Time Projection Chamber. The proton-antiproton production asymmetry is measured to study the baryon number transfer over a large rapidity interval in SD process. In addition, $K/\pi$ ratio is measured, showing a larger strangeness production at $p_T>0.5$ GeV/$\mathit{c}$ compared to measurements in inclusive proton-proton collisions.
