Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows
Published in Machine Learning and the Physical Sciences @ NeurIPS 2022, 2022
Recommended citation: Willmann A.,Cabadag J.C.,Chang Y.-Y.,Pausch R.,Ghaith A.,Debus A.,Irman A., Bussmann M., Schramm U., Hoffmann N. (2022). Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows. Machine Learning and the Physical Sciences @ NeurIPS 2022. https://ml4physicalsciences.github.io/2022/files/NeurIPS_ML4PS_2022_98.pdf
Understanding and control of Laser-driven Free Electron Lasers remain to be diffi- cult problems that require highly intensive experimental and theoretical research. The gap between simulated and experimentally collected data might complicate studies and interpretation of obtained results. In this work we developed a deep learning based surrogate that could help to fill in this gap. We introduce a surrogate model based on normalising flows for conditional phase-space representation of electron clouds in a FEL beamline. Achieved results let us discuss further benefits and limitations in exploitability of the models to gain deeper understanding of fundamental processes within a beamline. Download paper here
Recommended citation: Willmann A.,Cabadag J.C.,Chang Y.-Y.,Pausch R.,Ghaith A.,Debus A.,Irman A., Bussmann M., Schramm U., Hoffmann N. (2022). Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows. Machine Learning and the Physical Sciences @ NeurIPS 2022.