Machine-Learning for Injector Emittance Optimization

Poster contribution to the 15th Laser Advisory Commitee (LAC) at European XFEL/DESY Hamburg. Abstract OPAL-FEL is developing innovative machine learning algorithms to optimize emittance by solving the complexproblem of temporal cathode laser shaping. Preliminary simulations and results demonstrate promising outcomes,including the potential to reach photon energies even beyond 30 keV along with minimal emittance, acceleratingXFEL upgrades towards successful HDC/CW operation and unlocking new frontiers in attosecond science andPWFA research.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-02-21
DOI
https://doi.org/10.5281/zenodo.14906849
Primary Topic
Particle Accelerators and Free-Electron Lasers
Type
article
Field-Weighted Citation Impact
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article

Machine-Learning for Injector Emittance Optimization

Zenodo (CERN European Organization for Nuclear Research)
Particle Accelerators and Free-Electron Lasers
article

Machine-Learning for Injector Emittance Optimization

article en

Abstract

Poster contribution to the 15th Laser Advisory Commitee (LAC) at European XFEL/DESY Hamburg. Abstract OPAL-FEL is developing innovative machine learning algorithms to optimize emittance by solving the complexproblem of temporal cathode laser shaping. Preliminary simulations and results demonstrate promising outcomes,including the potential to reach photon energies even beyond 30 keV along with minimal emittance, acceleratingXFEL upgrades towards successful HDC/CW operation and unlocking new frontiers in attosecond science andPWFA research.

Zenodo (CERN European Organization for Nuclear Research)
Universität Hamburg (DE), Deutsches Elektronen-Synchrotron DESY (DE), Hamburg University of Technology (DE)
Reduced inequalities
Openalex Percentile: Top 55%
Particle Accelerators and Free-Electron Lasers
DOIOpen Access PDFOpenAlex
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