SciPost Phys. Proc. 18, 026 (2026) ·
published 29 January 2026
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With no evidence of direct production of beyond the Standard Model (BSM) particles at the TeV scale, deviations from the Standard Model (SM) can be explored systematically through Effective Field Theories (EFTs) such as the Standard Model EFT (SMEFT). The SMEFT, a framework for probing BSM effects, extends the SM by introducing higher-dimensional operators parameterized by Wilson coefficients. This contribution highlights three recent analyses using the ATLAS Run-2 dataset at a center-of-mass energy of $\sqrt{s}$ = 13 TeV with an integrated luminosity of 140 $\text{fb}^{-1}$. Combined measurements enhance sensitivity to Wilson coefficients, exploring the potential of SMEFT in the top quark sector.
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.
