Contributor info: Prof. Rafael Coelho Lopes de Sa

ATLAS Collaboration

SciPost Phys. 19, 155 (2025) · published 16 December 2025 | Toggle abstract · 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.

Rafael Coelho Lopes de Sá, Martina Javurkova, Matteo Lazzeretti, Raoul Röntsch

SciPost Phys. Comm. Rep. 12 (2025) · published 4 September 2025 | Toggle abstract · pdf

The measurement of the Higgs boson width is a critical test of the Standard Model, with significant implications for understanding electroweak symmetry breaking. Direct measurements are limited by detector resolution, but it can be measured with greater precision through a combined analysis of on-shell and off-shell Higgs boson production. While results for on-shell production have been computed to a very high accuracy, theoretical predictions for off-shell Higgs boson production are not as well controlled due to the breakdown of the heavy-top approximation and the large interference with non-resonant amplitudes. Seeking to understand and improve the theoretical control, we compare leading-order and next-to-leading-order plus parton shower differential cross-sections for signal, background, and full physical processes in off-shell Higgs boson production at the Large Hadron Collider, using Powheg, MadGraph, and Sherpa. We analyze the impact of higher-order quantum chromodynamics effects and theoretical uncertainties, highlighting differences between predictions using jet merging with parton showers, and those from next-to-leading order computations matched to parton showers. The results provide insights for improving theoretical predictions and their application to experimental measurements in the future.