ICALEPCS2021 - List of Authors (Maheshwari, M.)

Paper	Title	Page
WEPV020	Learning to Lase: Machine Learning Prediction of FEL Beam Properties	677
	A.E. Pollard, D.J. Dunning STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom M. Maheshwari STFC/DL, Daresbury, Warrington, Cheshire, United Kingdom
	Accurate prediction of longitudinal phase space and other properties of the electron beam are computationally expensive. In addition, some diagnostics are destructive in nature and/or cannot be readily accessed. Machine learning based virtual diagnostics can allow for the real-time generation of longitudinal phase space and other graphs, allowing for rapid parameter searches, and enabling operators to predict otherwise unavailable beam properties. We present a machine learning model for predicting a range of diagnostic screens along the accelerator beamline of a free-electron laser facility, conditional on linac and other parameters. Our model is a combination of a conditional variational autoencoder and a generative adversarial network, which generates high fidelity images that accurately match simulation data. Work to date is based on start-to-end simulation data, as a prototype for experimental applications.
	Poster WEPV020 [1.330 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV020
About •	Received ※ 10 October 2021 Revised ※ 22 October 2021 Accepted ※ 28 December 2021 Issue date ※ 25 February 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Learning to Lase: Machine Learning Prediction of FEL Beam Properties

677

A.E. Pollard, D.J. Dunning
STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
M. Maheshwari
STFC/DL, Daresbury, Warrington, Cheshire, United Kingdom

Accurate prediction of longitudinal phase space and other properties of the electron beam are computationally expensive. In addition, some diagnostics are destructive in nature and/or cannot be readily accessed. Machine learning based virtual diagnostics can allow for the real-time generation of longitudinal phase space and other graphs, allowing for rapid parameter searches, and enabling operators to predict otherwise unavailable beam properties. We present a machine learning model for predicting a range of diagnostic screens along the accelerator beamline of a free-electron laser facility, conditional on linac and other parameters. Our model is a combination of a conditional variational autoencoder and a generative adversarial network, which generates high fidelity images that accurately match simulation data. Work to date is based on start-to-end simulation data, as a prototype for experimental applications.

Poster WEPV020 [1.330 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV020

About •

Received ※ 10 October 2021 Revised ※ 22 October 2021 Accepted ※ 28 December 2021 Issue date ※ 25 February 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)