Work

Machine-Learning for Injector Emittance Optimization

Year: 2025

Type: article

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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14906848

Authors: Klemps, Alexander, Ilia, Denis, Tünnermann, Henrik, Chen, Ye, Hartl, Ingmar, Cai, Meng

Institutions: Hamburg University of Technology, Deutsches Elektronen-Synchrotron DESY

Language: English

Topic: Particle Accelerators and Free-Electron Lasers

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6930845324

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