DOAJ Open Access 2024

R&D of the EM Calorimeter Energy Calibration with Machine Learning based on the low-level features of the Cluster

Morimasa Suzuna Iwasaki Masako Suehara Taikan Tanaka Junichi Saito Masahiko +4 lainnya

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Abstrak

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.

Topik & Kata Kunci

Physics

Penulis (9)

Morimasa Suzuna

Iwasaki Masako

Suehara Taikan

Tanaka Junichi

Saito Masahiko

Nagahara Hajime

Nakashima Yuta

Takemura Noriko

Nakano Takashi

Format Sitasi

APA MLA BibTeX

Suzuna, M., Masako, I., Taikan, S., Junichi, T., Masahiko, S., Hajime, N. et al. (2024). R&D of the EM Calorimeter Energy Calibration with Machine Learning based on the low-level features of the Cluster. https://doi.org/10.1051/epjconf/202431503012

Akses Cepat

Lihat di Sumber doi.org/10.1051/epjconf/202431503012

Informasi Jurnal

Tahun Terbit: 2024
Sumber Database: DOAJ
DOI: 10.1051/epjconf/202431503012
Akses: Open Access ✓