Speaker
Sayantan Mukherjee
(Hiroshima University)
Description
The information on phase space in all six dimensions is required for various accelerator experiments. We developed an algorithm based on Convolutional Neural Network (CNN) that can be used instead of the traditional back projection techniques because it is less computationally intensive and has a simple architecture. Our method has shown consistency with the simulation, and we plan to validate it on data taken at the KEK–Superconducting Test Facility (STF).
| Region represented | Asia |
|---|---|
| Paper preparation format | Word |
Author
Sayantan Mukherjee
(Hiroshima University)
Co-authors
Masao Kuriki
(Hiroshima University)
Zachary Liptak
(Hiroshima University)
Hitoshi Hayano
(High Energy Accelerator Research Organization)
Masafumi Fukuda
(High Energy Accelerator Research Organization)
Masakazu Kurata
(High Energy Accelerator Research Organization)
Naoto Yamamoto
(High Energy Accelerator Research Organization)
Xiuguang Jin
(High Energy Accelerator Research Organization)
Yasuchika Yamamoto
(High Energy Accelerator Research Organization)
Kazuyuki Sakaue
(The University of Tokyo)
