Software compensation for highly granular calorimeters using machine learning
CALICE collaboration; Lai, S.; Utehs, J.; Wilhahn, A.; Bach, O.; Brianne, E.; Ebrahimi, A.; Gadow, K.; Göttlicher, P.; Hartbrich, O.; Heuchel, D.; Irles, A.; Krüger, K.; Kvasnicka, J.; Lu, S.; Neubüser, C.; Provenza, A.; Reinecke, M.; Sefkow, F.; ... mehrSchuwalow, S.; De Silva, M.; Sudo, Y.; Tran, H. L.; Buhmann, E.; Garutti, E.; Ḣuck, S.; Kasieczka, G.; Martens, S.; Rolph, J.; Wellhausen, J.; Blazey, G. C.; Dyshkant, A.; Francis, K.; Zutshi, V.; Bilki, B.; Northacker, D.; Onel, Y.; Hummer, F. 1; Simon, F. 1; Kawagoe, K.; Onoe, T.; Suehara, T.; Tsumura, S.; Yoshioka, T.; Fouz, M. C.; Emberger, L.; Graf, C.; Wagner, M.; Pöschl, R.; Richard, F.; Zerwas, D.; Boudry, V.; Brient, J-C.; Nanni, J.; Videau, H.; Liu, L.; Masuda, R.; Murata, T.; Ootani, W.; Takatsu, T.; Tsuji, N.; Chadeeva, M.; Danilov, M.; Korpachev, S.; Rusinov, V.
1 Institut für Prozessdatenverarbeitung und Elektronik (IPE), Karlsruher Institut für Technologie (KIT)
Abstract:
A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied.
Zugehörige Institution(en) am KIT |
Institut für Prozessdatenverarbeitung und Elektronik (IPE) |
Publikationstyp |
Zeitschriftenaufsatz |
Publikationsmonat/-jahr |
04.2024 |
Sprache |
Englisch |
Identifikator |
ISSN: 1748-0221
KITopen-ID: 1000170679 |
Erschienen in |
Journal of Instrumentation |
Verlag |
Institute of Physics Publishing Ltd (IOP Publishing Ltd) |
Band |
19 |
Heft |
04 |
Seiten |
Art.-Nr.: P04037 |
Vorab online veröffentlicht am |
26.04.2024 |
Schlagwörter |
Large detector-systems performance, Pattern recognition, cluster finding, calibration and fitting methods, Performance of High Energy Physics Detectors |
Nachgewiesen in |
Web of Science Dimensions Scopus
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Relationen in KITopen |
- Verweist auf
- Software compensation for highly granular calorimeters using machine learning. CALICE collaboration; Lai, S.; Utehs, J.; Wilhahn, A.; Bach, O.; Brianne, E.; Ebrahimi, A.; Gadow, K.; Göttlicher, P.; Hartbrich, O.; Heuchel, D.; Irles,... (2024) Forschungsbericht/Preprint (1000170702)
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