RL4AA Collaboration

Scheme of the FERMI FEL seed laser alignmentset up

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....

General feedback scheme using the CSR powersignal to construct both, the state and reward signal of the Markov decision process (MDP).

Feedback Design for Control of the Micro-Bunching Instability Based on Reinforcement Learning

T. Boltz, M. Brosi, E. Bründermann, B. Haerer, P. Kaiser, C. Pohl, P. Schreiber, M. Yan,T. Asfour, A.-S. Müller Karlsruhe Insitute of Technology KIT 10th International Particle Accelerator Conference Abstract The operation of ring-based synchrotron light sourceswith short electron bunches increases the emission of co-herent synchrotron radiation (CSR) in the THz frequencyrange. However, the micro-bunching instability resultingfrom self-interaction of the bunch with its own radiationfield limits stable operation with constant intensity of CSRemission to a particular threshold current....

Using a neural network control policy for rapid switching between beam parameters in an FEL

A. L. Edelen1, S. G. Biedron2, J. P. Edelen3, S. V. Milton4, P. J. M. van der Slot5 1Colorado State University, 2Element Aero, 3Fermi National Accelerator Laboratory, 4Los Alamos National Laboratory, 5University of Twente 38th International Free Electron Laser Conference Abstract FEL user facilities often must accommodate requests for a variety of beam parameters. This usually requires skilled operators to tune the machine, reducing the amount of available time for users....

General setup for the neural network model.

Using Neural Network Control Policies For Rapid Switching Between Beam Parameters in a Free Electron Laser

A. L. Edelen1, S. G. Biedron2, J. P. Edelen3, S. V. Milton4, P. J. M. van der Slot5 1Colorado State University, 2Element Aero, 3Fermi National Accelerator Laboratory, 4Los Alamos National Laboratory, 5University of Twente Workshop on Deep Learning for Physical Sciences at the Conference on Neural Information Processing Systems 2017 Abstract Free Electron Laser (FEL) facilities often must accommodate requests for a varietyof electron beam parameters in order to supply scientific users with appropriatephoton beam characteristics....

Orbit Correction Studies Using Neural Networks

E. Meier, Y.-R. E. Tan, G. S. LeBlanc Australian Synchrotron 3rd International Particle Accelerator Conference Abstract This paper reports the use of neural networks for orbitcorrection at the Australian Synchrotron Storage Ring. Theproposed system uses two neural networks in an actor-criticscheme to model a long term cost function and computeappropriate corrections. The system is entirely based onthe history of the beam position and the actuators, i.e. thecorrector magnets, in the storage ring....