<ul><li>Experiments at LCLS-II-HE are becoming increasingly complex due to the enhancement of brightness in light sources like APS and LCLS upgrades.</li><li>The proposed experiments will require precise X-ray beam control over long distances to handle a significant increase in data production rate.</li><li>Real-time active feedback control and optimized data processing pipelines are essential to extract meaningful scientific insights from the vast amount of data generated.</li><li>SLAC is developing a Machine Learning-driven strategy to optimize processes and extract real-time knowledge from electron accelerators to X-ray optics for enhanced scientific outcomes.</li></ul>

A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE

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