Maren Stratmann, on behalf of the ATLAS collaboration
SciPost Phys. Proc. 18, 006 (2026) ·
published 29 January 2026
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The $t$-channel is the dominant production channel for single top-quarks at the LHC. The total cross section of this process is measured by ATLAS in proton-proton collisions at a center-of-mass energy $\sqrt{s}=13$ TeV. The production cross sections for single top-quarks and single top-antiquarks are measured to be $\sigma_{tq}=\text{137}^{+8}_{-8}\,$pb and $\sigma_{\bar{t}q}=\text{84}^{+6}_{-5}\,$pb. For the combined cross section and the ratio of the cross sections $R_t$, $\sigma_{tq+\bar{t}q}=\text{221}^{+13}_{-13}\,$pb and $R_t=\text{1.636}^{+0\text{.}036}_{-0\text{.}034}$ are obtained. As interpretations of the measurement, limits are set on the EFT operator $O_{Qq}^{3,1}$ and on the CKM matrix elements $|V_{td}|$, $|V_{ts}|$ and $|V_{tb}|$.
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.
