Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics.

Longitudinal phase space (LPS) provides a critical information about electron beam dynamics for various scientific applications. For example, it can give insight into the high-brightness X-ray radiation from a free electron laser. Existing diagnostics are invasive, and often times cannot operate at the required resolution. In this work we present a machine learning-based Virtual Diagnostic (VD) tool to accurately predict the LPS for every shot using spectral information collected non-destructively from the radiation of relativistic electron beam. We demonstrate the tool's accuracy for three different case studies with experimental or simulated data. For each case, we introduce a method to increase the confidence in the VD tool. We anticipate that spectral VD would improve the setup and understanding of experimental configurations at DOE's user facilities as well as data sorting and analysis. The spectral VD can provide confident knowledge of the longitudinal bunch properties at the next generation of high-repetition rate linear accelerators while reducing the load on data storage, readout and streaming requirements.

Bayesian Optimization of a Free-Electron Laser.

The Linac coherent light source x-ray free-electron laser is a complex scientific apparatus which changes configurations multiple times per day, necessitating fast tuning strategies to reduce setup time for successive experiments. To this end, we employ a Bayesian approach to maximizing x-ray laser pulse energy by controlling groups of quadrupole magnets. A Gaussian process model provides probabilistic predictions for the machine response with respect to control parameters, enabling a balance of exploration and exploitation in the search for the global optimum. We show that the model parameters can be learned from archived scans, and correlations between devices can be extracted from the beam transport. The result is a sample-efficient optimization routine, combining both historical data and knowledge of accelerator physics to significantly outperform existing optimizers.

Amplification of flat laser pulse train.

We present modeling and measurements of flattop amplification of a laser pulse train in a diode pumped Nd:YLF system. We establish a theoretical model, accounting for the transverse Gaussian shape of an amplified laser beam, in order to explain remaining slopes in the pulse train energy. The influence of the transverse Gaussian shape on the train's flatness has been experimentally verified. Based on the model we are able to increase the total amplification of a long train of infrared seed beam in the drive laser system at the Fermilab Accelerator Science and Technology facility. The single-pass amplifier improvements resulted in a gain of ∼7 with flat output pulse train for up to 1000 seed pulses.