Speakers
Description
Planned upgrades of the European X-Ray Free Electron Laser (Eu-
XFEL) 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 emit-
tance of the electron gun. Within the OPAL-FEL project, we are addressing this
challenge 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.
Our approach centers on the application of deep learning techniques to create an
inverse model that predicts optimal parameter configurations for the photoin-
jector, enabling targeted control of beam emittance. This methodology involves
generating synthetic training data through comprehensive beam dynamics sim-
ulations and introduces a machine learning-based strategy for temporal pulse
shaping, accommodating a broad family of pulse distributions beyond flattop
and Gaussian shapes.
We present results from trained neural networks with various architectures and
establish a theoretical foundation for the invertibility of the forward model by
connecting our approach to the theory of inverse problems. In particular, we
draw on Whitney’s embedding theorem within the framework of attractor re-
construction to validate model invertibility.
Leveraging extensive simulations, data-driven modeling and theoretical insights,
our approach offers a robust pathway for improved optimization and control of
photoinjector parameters in CW mode, with potential applications for further
advancements in FEL performance.
