Contributor info: Prof. Mogens Dam

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.

Mogens Dam

SciPost Phys. Proc. 1, 041 (2019) · published 21 February 2019 | Toggle abstract · pdf

The future FCC-ee collider is designed to deliver $\mathrm{e^+e^-}$ collisions to study with ultimate precision the Z, W, and Higgs bosons, and the top quark. In a high-statistics scan around the Z pole, $1.3\times 10^{11}$ events $\mathrm{Z}\to\tau\tau$ will be produced, the largest sample of $\tau\tau$ events foreseen at any lepton collider. With their large boost, $\tau$ leptons from Z decays are particularly well suited for precision measurements. The focus of this report is on tests of lepton universality from precision measurement of $\boldsymbol{\tau}$ properties and on tests of charged lepton flavour violation in Z decays and in $\tau$ decays. In both of these areas, FCC-ee promises sensitivities well beyond present experimental limits.