| Issue |
EPJ Web Conf.
Volume 315, 2024
International Workshop on Future Linear Colliders (LCWS2024)
|
|
|---|---|---|
| Article Number | 03012 | |
| Number of page(s) | 5 | |
| Section | Detector | |
| DOI | https://doi.org/10.1051/epjconf/202431503012 | |
| Published online | 18 December 2024 | |
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
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