LINAC2022 - List of Keywords (FEM)

Paper	Title	Other Keywords	Page
MOPOPA11	Laser-to-RF Synchronisation Drift Compensation for the CLARA test facility	laser, detector, electron, timing	87
	J. Henderson, A.J. Moss, E.W. Snedden STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom A.C. Dexter Cockcroft Institute, Lancaster University, Lancaster, United Kingdom
	Femtosecond synchronisation between charged particle beams and external laser systems is a significant challenge for modern particle accelerators. To achieve femtosecond synchronisation of the CLARA electron beam and end user laser systems will require tight synchronisation of several accelerator subsystems. This paper reports on a method to compensate for environmentally driven long-term drift in Laser-RF phase detection systems.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-MOPOPA11
About •	Received ※ 22 August 2022 — Revised ※ 26 August 2022 — Accepted ※ 01 September 2022 — Issue date ※ 15 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WE2AA04	Data Analysis and Control of an MeV Ultrafast Electron Diffraction System using Machine Learning	electron, network, real-time, experiment	650
	T.B. Bolin, S. Biedron, M.A. Fazio, M. Martínez-Ramón, S.I. Sosa Guitron UNM-ECE, Albuquerque, USA M. Babzien, M.G. Fedurin, J.J. Li, M.A. Palmer BNL, Upton, New York, USA S. Biedron Element Aero, Chicago, USA S. Biedron UNM-ME, Albuquerque, New Mexico, USA
	MeV ultrafast electron diffraction (MUED) is a pump-probe material characterization technique to study ultrafast lattice dynamics with high temporal and spatial resolution. It is a relatively young technology that has the potential to shed light onto some of the most puzzling problems in physics. This complex instrument can be advanced into a turn-key high-throughput tool with the aid of machine learning (ML) mechanisms together with high-performance computing. The MUED instrument located in the Accelerator Test Facility of Brookhaven National Laboratory was employed in this work to test different ML approaches for both data analysis and control. We characterized three materials using MUED: graphite, black phosphorous and gold thin films. Diffraction patterns were acquired in single shot mode and different ML methodologies were applied to reduce image noise. Convolutional neural network autoenconder and variational autoenconder models were utilized to extract the noise features and increase the signal-to-noise ratio. The energy jitter of the electron beam was analyzed after noise reduction of the single shot diffraction patterns.

	please see instructions how to view/control embeded videos
	Slides WE2AA04 [12.865 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-WE2AA04
About •	Received ※ 30 August 2022 — Revised ※ 02 September 2022 — Accepted ※ 15 September 2022 — Issue date ※ 20 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOPOPA11

Laser-to-RF Synchronisation Drift Compensation for the CLARA test facility

laser, detector, electron, timing

87

J. Henderson, A.J. Moss, E.W. Snedden
STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
A.C. Dexter
Cockcroft Institute, Lancaster University, Lancaster, United Kingdom

Femtosecond synchronisation between charged particle beams and external laser systems is a significant challenge for modern particle accelerators. To achieve femtosecond synchronisation of the CLARA electron beam and end user laser systems will require tight synchronisation of several accelerator subsystems. This paper reports on a method to compensate for environmentally driven long-term drift in Laser-RF phase detection systems.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-MOPOPA11

About •

Received ※ 22 August 2022 — Revised ※ 26 August 2022 — Accepted ※ 01 September 2022 — Issue date ※ 15 September 2022

Cite •

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

WE2AA04

Data Analysis and Control of an MeV Ultrafast Electron Diffraction System using Machine Learning

electron, network, real-time, experiment

650

T.B. Bolin, S. Biedron, M.A. Fazio, M. Martínez-Ramón, S.I. Sosa Guitron
UNM-ECE, Albuquerque, USA
M. Babzien, M.G. Fedurin, J.J. Li, M.A. Palmer
BNL, Upton, New York, USA
S. Biedron
Element Aero, Chicago, USA
S. Biedron
UNM-ME, Albuquerque, New Mexico, USA

MeV ultrafast electron diffraction (MUED) is a pump-probe material characterization technique to study ultrafast lattice dynamics with high temporal and spatial resolution. It is a relatively young technology that has the potential to shed light onto some of the most puzzling problems in physics. This complex instrument can be advanced into a turn-key high-throughput tool with the aid of machine learning (ML) mechanisms together with high-performance computing. The MUED instrument located in the Accelerator Test Facility of Brookhaven National Laboratory was employed in this work to test different ML approaches for both data analysis and control. We characterized three materials using MUED: graphite, black phosphorous and gold thin films. Diffraction patterns were acquired in single shot mode and different ML methodologies were applied to reduce image noise. Convolutional neural network autoenconder and variational autoenconder models were utilized to extract the noise features and increase the signal-to-noise ratio. The energy jitter of the electron beam was analyzed after noise reduction of the single shot diffraction patterns.

please see instructions how to view/control embeded videos

Slides WE2AA04 [12.865 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-WE2AA04

About •

Received ※ 30 August 2022 — Revised ※ 02 September 2022 — Accepted ※ 15 September 2022 — Issue date ※ 20 September 2022

Cite •

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