Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network
Journal Article
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· SciPost Physics
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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.
- Research Organization:
- Univ. of Arizona, Tucson, AZ (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Grant/Contract Number:
- AC02-76SF00515; SC0009913
- OSTI ID:
- 3010222
- Journal Information:
- SciPost Physics, Journal Name: SciPost Physics Journal Issue: 6 Vol. 19; ISSN 2542-4653
- Publisher:
- SciPost FoundationCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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