
Planned upgrades of the European X-Ray Free Electron Laser (EuXFEL) target higher photon energy and a high duty-cycle operation up to CW-operation using a superconducting RF gun with lower gradient. An operation in this regime though critically depends on improvements of the beam slice emittance of the electron gun. Within the OPAL-FEL project, this challenge is addressed by developing a data-driven optimization framework for longitudinal drive laser shapes to minimize beam emittance, thereby ensuring the delivery of high-quality electron beams. Major part of the work builds on the generation and incorporation of simulated data, covering ultra-fast pulse shaping as well as beam dynamics in the photoinjector section. Within this dataset, synthetic data extracted from ASTRA simulations is served along with a comprehensive documentation on the sampling process and the selected features.
FEL, Machine Learning, EuXFEL, Emittance, DESY, ASTRA, OPAL-FEL, Photoinjector
FEL, Machine Learning, EuXFEL, Emittance, DESY, ASTRA, OPAL-FEL, Photoinjector
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