Speaker
Masako Iwasaki
(Osaka City Univ. / RCNP, Osaka Univ.)
Description
We have developed an energy calibration method for the ILC SiD EM calorimeter (ECAL), a sampling calorimeter consisting with 30 Silicon-Tungsten layers, using machine learning.
Our approach uses a deep neural network (DNN) in a regression problem to obtain the energy of the incident particle from the list of measured energy deposits (energy calibration).
The DNN is used to express the non-linear detector response and to get the particle ID information, electron or photon, in a particle-depend calibration.
We report on the status of the R&D and future plans.
Authors
Amanda Lynn Steinhebel
(University of Oregon (US))
Hajime Nagahara
(Institute for Datability Science, Osaka University)
Jan Fridolf Strube
(PNNL)
Jim Brau
(University of Oregon (US))
Koki Morikawa
(Osaka City Univ.)
Martin Breidenbach
(SLAC)
Masako Iwasaki
(Osaka City Univ. / RCNP, Osaka Univ.)
Noriko Takemura
(Institute for Datability Science, Osaka University)
Yusuke Naka
(Osaka City Univ.)
Yuta Nakashima
(Institute for Datability Science, Osaka University)
