| Article | |
| Report number | arXiv:2403.04632 |
| Title | Software Compensation for Highly Granular Calorimeters using Machine Learning |
| Author(s) |
Lai, S. (Gottingen U., II. Phys. Inst.) ; Utehs, J. (Gottingen U., II. Phys. Inst.) ; Wilhahn, A. (Gottingen U., II. Phys. Inst.) ; Bach, O. (DESY) ; Brianne, E. (DESY) ; Ebrahimi, A. (DESY) ; Gadow, K. (DESY) ; Göttlicher, P. (DESY) ; Hartbrich, O. (DESY) ; Heuchel, D. (DESY) ; Irles, A. (DESY) ; Krüger, K. (DESY) ; Kvasnicka, J. (DESY) ; Lu, S. (DESY) ; Neubüser, C. (DESY) ; Provenza, A. (DESY) ; Reinecke, M. (DESY) ; Sefkow, F. (DESY) ; Schuwalow, S. (DESY) ; De Silva, M. (DESY) ; Sudo, Y. (DESY) ; Tran, H.L. (DESY) ; Buhmann, E. (Hamburg U.) ; Garutti, E. (Hamburg U.) ; Huck, S. (Hamburg U.) ; Kasieczka, G. (Hamburg U.) ; Martens, S. (Hamburg U.) ; Rolph, J. (Hamburg U.) ; Wellhausen, J. (Hamburg U.) ; Blazey, G.C. (NIU, DeKalb) ; Dyshkant, A. (NIU, DeKalb) ; Francis, K. (NIU, DeKalb) ; Zutshi, V. (NIU, DeKalb) ; Bilki, B. (U. Iowa, Iowa City) ; Northacker, D. (U. Iowa, Iowa City) ; Onel, Y. (U. Iowa, Iowa City) ; Hummer, F. (KIT, Karlsruhe) ; Simon, F. (KIT, Karlsruhe) ; Kawagoe, K. (Kyushu U.) ; Onoe, T. (Kyushu U.) ; Suehara, T. (Kyushu U.) ; Tsumura, S. (Kyushu U.) ; Yoshioka, T. (Kyushu U.) ; Fouz, M.C. (Madrid, CIEMAT) ; Emberger, L. (Munich, Max Planck Inst.) ; Graf, C. (Munich, Max Planck Inst.) ; Wagner, M. (Munich, Max Planck Inst.) ; Pöschl, R. (IJCLab, Orsay) ; Richard, F. (IJCLab, Orsay) ; Zerwas, D. (IJCLab, Orsay) ; Boudry, V. (LLR, Palaiseau) ; Brient, J-C. (Ecole Polytechnique) ; Nanni, J. (Ecole Polytechnique) ; Videau, H. (Ecole Polytechnique) ; Liu, L. (Tokyo U., ICEPP) ; Masuda, R. (Tokyo U., ICEPP) ; Murata, T. (Tokyo U., ICEPP) ; Ootani, W. (Tokyo U., ICEPP) ; Takatsu, T. (Tokyo U., ICEPP) ; Tsuji, N. (Tokyo U., ICEPP) ; Chadeeva, M. ; Danilov, M. ; Korpachev, S. ; Rusinov, V. |
| Publication | 2024-04 |
| Imprint | 2024-03-07 |
| Number of pages | 27 |
| In: | JINST 19 (2024) P04037 |
| DOI | 10.1088/1748-0221/19/04/P04037 |
| Subject category | physics.ins-det ; Detectors and Experimental Techniques |
| Accelerator/Facility, Experiment | CALICE |
| 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. |
| Copyright/License | preprint: (License: arXiv nonexclusive-distrib 1.0) publication: © 2024 The Author(s) (License: CC-BY-4.0) |
Corresponding record in: Inspire
Record created 2024-03-20, last modified 2024-08-05
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