%0 Journal Article
%T Software compensation for highly granular calorimeters using machine learning
%A CALICE Collaboration (Lai, S. et al
%A Irles, A.
%J Journal of Instrumentation
%D 2024
%V 19
%N 4
%I IOP Publishing Ltd
%@ 1748-0221
%G English
%F CALICECollaborationLai+Irles2024
%O WOS:001230094600001
%O exported from refbase (https://references.ific.uv.es/refbase/show.php?record=6128), last updated on Sun, 02 Jun 2024 13:09:54 +0000
%X 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.
%K Large detector-systems performance
%K Pattern recognition
%K cluster finding
%K calibration and fitting methods
%K Performance of High Energy Physics Detectors
%R 10.1088/1748-0221/19/04/P04037
%U https://arxiv.org/abs/2403.04632
%U https://doi.org/10.1088/1748-0221/19/04/P04037
%P P04037 - 28pp