The majority of user experiments at the high repetition-rate free electron laser (FEL) facility FLASH are of pump-probe type, combining the extreme ultraviolet (XUV) or soft x-ray radiation from the FEL with ultrashort pulses generated by optical lasers. In this contribution, we demonstrate the advantages of using high-power Yb:YAG lasers with subsequent nonlinear pulse compression stages based on multi-pass cells (MPC). The approach enables the combination of hundreds of kHz to MHz repetition-rates, hundreds of watts of average powers and excellent intensity stabilities. We present the characteristics of the MPC-based pump-probe laser at the FLASH plane-grating beamlines. Furthermore, we report pulse compression to 8.2 fs pulse duration and the seeding of an optical parametric amplifier generating mid-IR radiation tunable from 1.4 µm to 16 µm.
Pump-probe lasers for FELs must provide stable pulse energy, timing, and beam position. Here, we show active stabilization of beam pointing fluctuations using a combination of classic control, artificial intelligence, and machine learning techniques. As our laser system operates in 10 Hz burst mode, fast feedback is not possible. Therefore, we have to utilize the available information as efficiently as possible. Beam pointing fluctuations of laser beams can be described by 4 parameters – as the actuators (motorized mirrors) are not orthogonal we need a model to calculate the required actuator movements. As effects such as motor acceleration are not easy to capture in a physical model, we use an automated data-driven approach. The measurement of the beam position is noisy, so we use a Kalman-Filter, which also integrates our feedback actions to smooth the output. Finally, we use an integrating controller to control the beam. The final transport of the beam to the pump-probe experiment introduces additional drifts, but during user operation, the beam position at the interaction point cannot be measured. We, therefore, measure correlated properties such as temperature, humidity, and air pressure and trained a machine learning model to predict its location. Integrating this model in a feed-forward loop could improve the RMS error of the beam position by 63% in the x-axis and 8% in the y-axis.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.