Energy reconstruction in a liquid argon calorimeter cell using convolutional neural networks
| dc.contributor.author | Lefebvre, Michel | |
| dc.contributor.author | Polson, Luke | |
| dc.contributor.author | Leonid, Kurchaninov | |
| dc.date.accessioned | 2022-01-17T18:57:21Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022-01-04 | |
| dc.description | The authors would like to thank Dr Steffen Stärz and Alessandro Ambler for fruitful discussions. | en_US |
| dc.description.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. | en_US |
| dc.description.embargo | 2023-01-04 | |
| dc.description.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Faculty | en_US |
| dc.description.sponsorship | Natural Sciences and Engineering Research Council of Canada, Compute Canada, and CMC Microsystems | en_US |
| dc.identifier.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 | en_US |
| dc.identifier.uri | https://doi.org/10.1088/1748-0221/17/01/P01002 | |
| dc.identifier.uri | http://hdl.handle.net/1828/13710 | |
| dc.language.iso | en | en_US |
| dc.publisher | Journal of Instrumentation | en_US |
| dc.subject | signal processing | |
| dc.subject | convolutional neural networks | |
| dc.subject | liquid argon calorimeter | |
| dc.subject | ATLAS detector | |
| dc.subject | Victoria Subatomic Physics and Accelerator Research Centre (VSPA) | |
| dc.subject.department | Department of Physics and Astronomy | |
| dc.title | Energy reconstruction in a liquid argon calorimeter cell using convolutional neural networks | en_US |
| dc.type | Article | en_US |