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| Home > Publications database > Machine Learning Enhanced Beam Pointing Stabilization for the Pump-Probe Laser at the FLASH FEL Facility |
| Home > Publications database > Machine Learning Enhanced Beam Pointing Stabilization for the Pump-Probe Laser at the FLASH FEL Facility |
| Conference Presentation | PUBDB-2023-07244 |
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2023
Abstract: 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.
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