Work

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

Year: 2024

Type: article

Abstract: \nWe 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.\n

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/epjconf/202431503012/pdf

Authors: Suzuna Morimasa, Masako Iwasaki, Taikan Suehara, Junichi Tanaka, Masahiko Saito, Hajime Nagahara, Yuta Nakashima, Noriko Takemura, Takashi Nakano

Institutions: Osaka Metropolitan University, The University of Tokyo, The University of Osaka, Kyushu Institute of Technology

Language: English

Topic: Particle Detector Development and Performance

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: Japan Society for the Promotion of Science, Pacific Northwest National Laboratory, SLAC National Accelerator Laboratory

Award: Exploration of the particle physics frontier at the Super B-factory and cultivation o f young researchers

OpenAlex ID: W6921875265

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