Carmen Diez Pardos, on behalf of the ATLAS collaboration
SciPost Phys. Proc. 18, 010 (2026) ·
published 29 January 2026
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Cross-section measurements of the associated production of a top quark pair and a photon ($t\bar{t}\gamma$) are performed with an integrated luminosity of 140 fb$^{-1}$ of proton−proton collisions at a centre-of-mass energy of 13 TeV collected by the ATLAS detector at the LHC. The measurement focuses on $t\bar{t}\gamma$ topologies where the photon is radiated from an initial-state parton or one of the top quarks. The differential cross-sections are measured for variables characterising the photon, lepton and jet kinematic properties. The distribution of the photon transverse momentum is used to constrain effective field theory operators related to the electroweak dipole moments of the top quark.
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.
Dr Diez Pardos: "Dear referee, thank you fo..."
in Submissions | report on Measurements of inclusive and differential cross-sections of $t\bar{t}γ$ production in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector