https://doi.org/10.1051/epjconf/202431503012
R&D of the EM Calorimeter Energy Calibration with Machine Learning based on the low-level features of the Cluster
1 Osaka Metropolitan University Graduate School of Science, Osaka, Japan
2 Osaka University Research Center for Nuclear Physics (RCNP), Osaka, Japan
3 Osaka University Institute for Datability Science (IDS), Osaka, Japan
4 The University of Tokyo, International Center for Elementary Partilce Physics (ICEPP), Tokyo Japan
5 Kyushu Institute of Technology, Fukuoka Japan
* e-mail: sd23697f@st.omu.ac.jp
** e-mail: masako.iwasaki@omu.ac.jp
Published online: 18 December 2024
We have developed an energy calibration method using machine learning for the ILC electromagnetic (EM) calorimeter (ECAL), a sampling calorimeter consisting of Silicon-Tungsten layers. In this method, we use a deep neural network (DNN) for a regression to determine the energy of incident EM particles, improving the energy calibration resolution of the ECAL. The DNN architecture takes cluster hit data as low-level features of the cluster. In this paper, we report the status of our R&D and present results on energy calibration accuracy.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
