PT Journal
AU CALICE Collaboration (Lai, Sea
   Irles, A
TI Software compensation for highly granular calorimeters using machine learning
SO Journal of Instrumentation
JI J. Instrum.
PY 2024
BP P04037 - 28pp
VL 19
IS 4
DI 10.1088/1748-0221/19/04/P04037
LA English
DE Large detector-systems performance; Pattern recognition; cluster finding; calibration and fitting methods; 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.
ER