paper • The following article is Open access

Software compensation for highly granular calorimeters using machine learning

, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and

Published 26 April 2024 • © 2024 The Author(s)
, , Citation S. Lai et al 2024 JINST 19 P04037 DOI 10.1088/1748-0221/19/04/P04037

Download Article PDF

Article metrics

23 Total downloads

Share this article

Author e-mails

jack.rolph@desy.de

Author affiliations

1 II. Physikalisches Institut, Georg-August-Universität Göttingen, Friedrich-Hund-Platz 1, Göttingen D-37077, Germany

2 DESY, Notkestrasse 85, Hamburg D-22603, Germany

3 Universität Hamburg, Physics Department, Institut für Experimentalphysik, Luruper Chaussee 149, Hamburg 22761, Germany

4 NICADD, Northern Illinois University, Department of Physics, DeKalb, IL 60115, U.S.A.

5 Department of Physics and Astronomy, University of Iowa, 203 Van Allen Hall, Iowa City, IA 52242-1479, U.S.A.

6 Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, 27 Kaiserstr. 12, Karlsruhe D-76131, Germany

7 Department of Physics and Research Center for Advanced Particle Physics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan

8 CIEMAT, Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas, Madrid, Spain

9 Max-Planck-Institut für Physik, Föhringer Ring 6, Munich D-80805, Germany

10 CNRS/IN2P3, IJCLab, Université Paris-Saclay, Orsay 91405, France

11 Laboratoire Leprince-Ringuet (LLR), CNRS, École polytechnique, Institut Polytechnique de Paris, Palaiseau F-91120, France

12 ICEPP, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

13 Affiliated with an institute that has signed the CALICE MOU

Author notes

14 Now at Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37830, U.S.A.

15 Now at Instituto de Física Corpuscular, Parque Científico, Catedrático José Beltrán, 2, Paterna E-46980, Spain.

16 Also at Institute of Physics, The Czech Academy of Sciences, Prague, Czech Republic.

17 Deceased.

18 Now at ICEPP, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.

ORCID iDs

Dates

  1. Received 11 January 2024
  2. Accepted 6 March 2024
  3. Published

Buy this article in print

Journal RSS
Sign up for new issue notifications
1748-0221/19/04/P04037

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.

Export citation and abstract BibTeX RIS

Previous article in issue
Next article in issue

Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Please wait… references are loading.
10.1088/1748-0221/19/04/P04037