Software Compensation for Highly Granular Calorimeters using Machine Learning
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.
Cite
@article{arxiv.2403.04632,
title = {Software Compensation for Highly Granular Calorimeters using Machine Learning},
author = {S. Lai and J. Utehs and A. Wilhahn and O. Bach and E. Brianne and A. Ebrahimi and K. Gadow and P. Göttlicher and O. Hartbrich and D. Heuchel and A. Irles and K. Krüger and J. Kvasnicka and S. Lu and C. Neubüser and A. Provenza and M. Reinecke and F. Sefkow and S. Schuwalow and M. De Silva and Y. Sudo and H. L. Tran and E. Buhmann and E. Garutti and S. Huck and G. Kasieczka and S. Martens and J. Rolph and J. Wellhausen and G. C. Blazey and A. Dyshkant and K. Francis and V. Zutshi and B. Bilki and D. Northacker and Y. Onel and F. Hummer and F. Simon and K. Kawagoe and T. Onoe and T. Suehara and S. Tsumura and T. Yoshioka and M. C. Fouz and L. Emberger and C. Graf and M. Wagner and R. Pöschl and F. Richard and D. Zerwas and V. Boudry and J-C. Brient and J. Nanni and H. Videau and L. Liu and R. Masuda and T. Murata and W. Ootani and T. Takatsu and N. Tsuji and M. Chadeeva and M. Danilov and S. Korpachev and V. Rusinov},
journal= {arXiv preprint arXiv:2403.04632},
year = {2024}
}