Contributor info: Dr Charlie Chen

Charlie Chen, on behalf of the ATLAS collaboration

SciPost Phys. Proc. 18, 008 (2026) · published 29 January 2026 | Toggle abstract · pdf

The ATLAS collaboration at the LHC has published inclusive cross-section measurements for the single-top and $t\overline{t}$ production modes at center-of-mass energies of $\sqrt{s} = 5.02, 8.16$, $13$, and $13.6$ TeV. Single-top measurements are conducted in the $t$-channel and $tW$ channel. In addition to the nominal cross-section measurements, various measurements of other interesting observables such as the $V_{tb}$ element of the Cabibbo Kobayashi Maskawa (CKM) quark-mixing matrix, the ratio of the inclusive cross-sections between $tq$ and $t\overline{q}$, the ratio of inclusive cross-sections between $t\overline{t}$ and $Z→ \ell\ell$, and the nuclear modification factor (defined as the ratio of the inclusive $t\overline{t}$ cross section in heavy-ion collisions to the inclusive $t\overline{t}$ cross-section in $pp$ collisions) are also reported. These results are compared to their corresponding SM predictions, calculated at (N)NLO in QCD. All results are in good agreement with SM predictions.

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.