TUPM008

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URL	https://doi.org/10.18429/JACoW-IPAC2025-TUPM008
Title	Six-dimensional phase space reconstruction with multimodal CNN
Authors	S. Mukherjee, M. Kuriki, Z. Liptak Hiroshima University H. Hayano, M. Fukuda, M. Kurata, N. Yamamoto, X. Jin, Y. Yamamoto High Energy Accelerator Research Organization K. Sakaue The University of Tokyo
Abstract	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).
Paper	download: TUPM008.pdf
Cite	BibTeX, LaTeX, Text/Word, RIS, EndNote
Conference	16th International Particle Accelerator Conference
Series
Location	Taipei, Taiwan
Date	01-06 Jun 2025
Publisher	JACoW Publishing, Geneva, Switzerland
Editorial Board	* Ming-Chyuan Lin - National Synchrotron Radiation Research Center * Yoichi Sato - KEK * Jui-Che Huang - National Synchrotron Radiation Research Center * David Button - Australian Nuclear Science and Technology Organisation * Ping-Shun Chuang - National Synchrotron Radiation Research Center
Online ISBN	978-3-95450-248-6
Online ISSN	2673-5490
Received	29 May 2025
Revised	05 June 2025
Accepted	05 June 2025
Issued	05 November 2025
DOI	10.18429/JACoW-IPAC2025-TUPM008
Pages	1182-1185
Copyright	Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.