Energy reconstruction in a liquid argon calorimeter cell using convolutional neural networks. (4th January 2022)

• Record Type: Journal Article
• Authors: Polson, L.; Kurchaninov, L.; Lefebvre, M.
• Is Part Of: Journal of instrumentation. Volume 17:Number 1(2022)
• Publication Date: 2022-01-04
• Subjects: Performance of High Energy Physics Detectors -- Data processing methods -- Calorimeters -- Noble liquid detectors (scintillation, ionization, double-phase); Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/17/01/P01002
• Languages: English
• ISSNs: 1748-0221

Abstract

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.