Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network


Aad G., Aakvaag E., Abbott B., Abdelhameed S., Abeling K., Abicht N. J., ...More

SCIPOST PHYSICS, vol.19, no.6, 2025 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 19 Issue: 6
  • Publication Date: 2025
  • Doi Number: 10.21468/scipostphys.19.6.155
  • Journal Name: SCIPOST PHYSICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Directory of Open Access Journals
  • Istanbul University Affiliated: Yes

Abstract

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.