JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{bolin:linac2022-we2aa04,
author = {T.B. Bolin and M. Babzien and S. Biedron and M.A. Fazio and M.G. Fedurin and J.J. Li and M. Martínez-Ramón and M.A. Palmer and S.I. Sosa Guitron},
% author = {T.B. Bolin and M. Babzien and S. Biedron and M.A. Fazio and M.G. Fedurin and J.J. Li and others},
% author = {T.B. Bolin and others},
title = {{Data Analysis and Control of an MeV Ultrafast Electron Diffraction System using Machine Learning}},
booktitle = {Proc. LINAC'22},
% booktitle = {Proc. 31st International Linear Accelerator Conference (LINAC'22)},
pages = {650--652},
eid = {WE2AA04},
language = {english},
keywords = {electron, network, real-time, FEM, experiment},
venue = {Liverpool, UK},
series = {International Linear Accelerator Conference},
number = {31},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {09},
year = {2022},
issn = {2226-0366},
isbn = {978-3-95450-215-8},
doi = {10.18429/JACoW-LINAC2022-WE2AA04},
url = {https://jacow.org/linac2022/papers/we2aa04.pdf},
abstract = {{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.}},
}