TY  - JOUR
AU  - CALICE Collaboration (Lai, S. et al
AU  - Irles, A.
PY  - 2024
DA  - 2024//
TI  - Software compensation for highly granular calorimeters using machine learning
T2  - J. Instrum.
JO  - Journal of Instrumentation
SP  - P04037 - 28pp
VL  - 19
IS  - 4
PB  - IOP Publishing Ltd
KW  - Large detector-systems performance
KW  - Pattern recognition
KW  - cluster finding
KW  - calibration and fitting methods
KW  - Performance of High Energy Physics Detectors
AB  - 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.
SN  - 1748-0221
UR  - https://arxiv.org/abs/2403.04632
UR  - https://doi.org/10.1088/1748-0221/19/04/P04037
DO  - 10.1088/1748-0221/19/04/P04037
LA  - English
N1  - WOS:001230094600001
ID  - CALICECollaborationLai+Irles2024
ER  -