Generation of ultrahigh harmonics with a two-stage free electron laser and a seed laser

1997 ◽  
Vol 55 (5) ◽  
pp. 6002-6010 ◽  
Author(s):  
V. V. Goloviznin ◽  
P. W. van Amersfoort
Keyword(s):  
Free Electron ◽  
Free Electron Laser ◽  
Seed Laser ◽  
Two Stage

Chirped-beam two-stage free-electron laser for high-power femtosecond x-ray pulse generation

2002 ◽  
Vol 19 (8) ◽  
pp. 1782 ◽  
Author(s):  
Carl B. Schroeder ◽  
Claudio Pellegrini ◽  
Sven Reiche ◽  
John Arthur ◽  
Paul Emma
Keyword(s):  
High Power ◽  
Free Electron ◽  
Free Electron Laser ◽  
Pulse Generation ◽  
Two Stage ◽  
X Ray

Two-Stage Free Electron Laser Driven by an Induction Linac

1988 ◽  
pp. 171-176
Author(s):  
S. Kuruma ◽  
K. Mima ◽  
Y. Tsunawaki ◽  
N. Ohigashi ◽  
T. Akiba ◽  
...  
Keyword(s):  
Free Electron ◽  
Free Electron Laser ◽  
Two Stage ◽  
Induction Linac

The KMS Fusion, Inc. Two-Stage Free Electron Laser Program

1984 ◽  
Author(s):  
S. B. Segall ◽  
H. Takeda ◽  
S. Von Laven ◽  
P. Diament ◽  
J. F. Ward
Keyword(s):  
Free Electron ◽  
Free Electron Laser ◽  
Two Stage

scholarly journals Basic Reinforcement Learning Techniques to Control the Intensity of a Seeded Free-Electron Laser

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 781 ◽  
Author(s):  
Niky Bruchon ◽  
Gianfranco Fenu ◽  
Giulio Gaio ◽  
Marco Lonza ◽  
Finn Henry O’Shea ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Free Electron ◽  
Performance Optimization ◽  
Free Electron Laser ◽  
Classical Model ◽  
Particle Accelerator ◽  
Particle Accelerators ◽  
Seed Laser ◽  
Model Free ◽  
Performance Recovery

Optimal tuning of particle accelerators is a challenging task. Many different approaches have been proposed in the past to solve two main problems—attainment of an optimal working point and performance recovery after machine drifts. The most classical model-free techniques (e.g., Gradient Ascent or Extremum Seeking algorithms) have some intrinsic limitations. To overcome those limitations, Machine Learning tools, in particular Reinforcement Learning (RL), are attracting more and more attention in the particle accelerator community. We investigate the feasibility of RL model-free approaches to align the seed laser, as well as other service lasers, at FERMI, the free-electron laser facility at Elettra Sincrotrone Trieste. We apply two different techniques—the first, based on the episodic Q-learning with linear function approximation, for performance optimization; the second, based on the continuous Natural Policy Gradient REINFORCE algorithm, for performance recovery. Despite the simplicity of these approaches, we report satisfactory preliminary results, that represent the first step toward a new fully automatic procedure for the alignment of the seed laser to the electron beam. Such an alignment is, at present, performed manually.

Extreme UV generation with two-stage free electron laser

1984 ◽  
Author(s):  
I. Boscolo ◽  
W. B. Colson ◽  
V. Stagno
Keyword(s):  
Free Electron ◽  
Free Electron Laser ◽  
Two Stage ◽  
Extreme Uv

A traveling-wave ring resonator with Bragg deflectors in a two-stage terahertz free-electron laser

2014 ◽  
Vol 40 (9) ◽  
pp. 730-734 ◽  
Author(s):  
A. V. Arzhannikov ◽  
N. S. Ginzburg ◽  
G. G. Denisov ◽  
P. V. Kalinin ◽  
N. Yu. Peskov ◽  
...  
Keyword(s):  
Traveling Wave ◽  
Free Electron ◽  
Free Electron Laser ◽  
Ring Resonator ◽  
Two Stage
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