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Author (up) CALICE Collaboration (Lai, S. et al); Irles, A. url  doi
openurl 
  Title Software compensation for highly granular calorimeters using machine learning Type Journal Article
  Year 2024 Publication Journal of Instrumentation Abbreviated Journal J. Instrum.  
  Volume 19 Issue 4 Pages P04037 - 28pp  
  Keywords Large detector-systems performance; Pattern recognition; cluster finding; calibration and fitting methods; Performance of High Energy Physics Detectors  
  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.  
  Address [Lai, S.; Utehs, J.; Wilhahn, A.] Georg August Univ Gottingen, Phys Inst 2, Friedrich Hund Pl 1, D-37077 Gottingen, Germany, Email: jack.rolph@desy.de  
  Corporate Author Thesis  
  Publisher IOP Publishing Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1748-0221 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001230094600001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 6128  
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