Energy reconstruction in a liquid argon calorimeter cell using convolutional neural networks
Date
2022-01-04
Authors
Lefebvre, Michel
Polson, Luke
Leonid, Kurchaninov
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Instrumentation
Abstract
The liquid argon ionization current in a sampling calorimeter cell can be analyzed to determine the energy of detected particles. In practice, experimental artifacts such as pileup and electronic noise make the inference of energy from current a difficult process. The beam intensity of the Large Hadron Collider will be significantly increased during the Phase-II long shut-down of 2025–2027. Signal processing techniques that are used to extract the energy of detected particles in the ATLAS detector will suffer a significant loss in performance under these conditions. This paper compares the presently used optimal filter technique to convolutional neural networks for energy reconstruction in the ATLAS liquid argon hadronic end cap calorimeter. In particular, it is shown that convolutional neural networks trained with an appropriately tuned and novel loss function are able to outperform the optimal filter technique.
Description
The authors would like to thank Dr Steffen Stärz and Alessandro Ambler for fruitful discussions.
Keywords
signal processing, convolutional neural networks, liquid argon calorimeter, ATLAS detector, Victoria Subatomic Physics and Accelerator Research Centre (VSPA)
Citation
Polson, L., Kurchaninov, L., and Lefebvre, M. (2022). Energy reconstruction in a liquid argon calorimeter cell using convolutional neural networks. Journal of Instrumentation vol. 17. https://doi.org/10.1088/1748-0221/17/01/P01002