The paper proposes a start-to-end chained machine-learning framework for LCLS-II-HE and demonstrates that TuRBO Bayesian optimization aligns a hard X-ray split-and-delay system in minutes.
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A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE
The paper proposes a start-to-end chained machine-learning framework for LCLS-II-HE and demonstrates that TuRBO Bayesian optimization aligns a hard X-ray split-and-delay system in minutes.