Free-electron Laser Optimization with Reinforcement Learning

N. Bruchon¹, G. Gaio², G. Fenu¹, M. Lonza², F. A. Pellegrino¹, E. Salvato¹

¹University of Trieste, ²Elettra Sincrotrone Trieste

17th International Conference on Accelerator and Large Experimental Physics Control Systems

Abstract

Reinforcement Learning (RL) is one of the most promis-ing techniques in Machine Learning because of its modestcomputational requirements with respect to other algorithms.RL uses an agent that takes actions within its environmentto maximize a reward related to the goal it is designed toachieve. We have recently used RL as a model-free approachto improve the performance of the FERMI Free ElectronLaser. A number of machine parameters are adjusted tofind the optimum FEL output in terms of intensity and spec-tral quality. In particular we focus on the problem of thealignment of the seed laser with the electron beam, initiallyusing a simplified model and then applying the developedalgorithm on the real machine. This paper reports the resultsobtained and discusses pros and cons of this approach withplans for future applications.

Read the paper: https://accelconf.web.cern.ch/icalepcs2019/papers/wepha021.pdf

Contact: Niky Bruchon