Speaker
Suzuna Morimasa
(Osaka Metropolitan University)
Description
We have developed the energy calibration method by using the machine learning for the ILC EM calorimeter (ECAL), a sampling calorimeter consisting with Silicon-Tungsten layers.
In this method, we use deep neural network (DNN) to get the energy of the incident particle (energy calibration), as a regression problem,
to improve the energy calibration resolution of ECAL.
We have developed the DNN architecture where cluster hit data are input as low-level features of the cluster.
We'll report the status of the R&D.
| Apply for poster award | Yes |
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Primary author
Suzuna Morimasa
(Osaka Metropolitan University)
Co-authors
Hajime Nagahara
(The University of Osaka)
Junichi Tanaka
(CERN)
Masahiko Saito
(ICEPP, The University of Tokyo)
Masako Iwasaki
(Osaka City Univ. / RCNP, Osaka Univ.)
Noriko Takemura
(Kyushu Institute of Technology)
Taikan Suehara
(ICEPP, The University of Tokyo)
Takashi Nakano
(The University of Osaka)
Yuta Nakashima
(The University of Osaka)
