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URLhttps://doi.org/10.18429/JACoW-IPAC2024-TUPS77
TitleApplications of machine learning in ultrafast laser control
Authors
  • A. Aslam, S. Biedron, T. Bolin
    University of New Mexico
  • M. Babzien
    Brookhaven National Laboratory
AbstractIn our pursuit to tailor a precise electron bunch with a photoinjector, fine-tuning laser parameters, especially those influencing the photocathode pulse, is pivotal. Our ongoing research integrates machine learning, training neural networks with experimental data from ATF. The first approach involves generating a downstream photocurrent image to replicate the emission profile, serving as a fitness function for neural network training. The second approach employs an emittance scan during each iteration of the neural network-controlled laser profile, using magnetic optics and beam profile monitors, with calculated beam emittance as an additional fitness function. Our research aims to demonstrate the potential superiority of the neural network in achieving precise laser shaping for electron beam optimization. Leveraging real data, our goal is to reduce electron beam emittance through optimized laser profiles, underscoring the impactful applications of machine learning in advancing photoinjector technology.
Paperdownload: TUPS77.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference15th International Particle Accelerator Conference
Series
LocationNashville, TN
Date19-24 May 2024
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardFulvia Pilat - Oak Ridge National Laboratory Wolfram Fischer - Brookhaven National Laboratory Robert Saethre - Oak Ridge National Laboratory Petr Anisimov - Los Alamos National Laboratory Ivan Andrian - Elettra-Sincrotrone Trieste S.C.p.A.
Online ISBN978-3-95450-247-9
Online ISSN2673-5490
Received09 May 2024
Revised22 May 2024
Accepted22 May 2024
Issued01 July 2024
DOI10.18429/JACoW-IPAC2024-TUPS77
Pages1862-1864