Speaker
Description
Characterizing the full 6-dimensional phase-space distribution of beams from the LCLS-II photoinjector is essential for understanding and optimizing downstream accelerator performance. Long-term monitoring of this distribution is equally important for detecting drifts in machine state and implementing timely corrective actions. Continuous 6D characterization during routine operation demands reliable tomographic diagnostic measurements and fast, efficient reconstruction methods. In this work, we demonstrate the first fully autonomous 6D beam-tomography system deployed on a parasitic diagnostic line at LCLS-II. Using machine learning-based control algorithms, the system autonomously configures and executes tomographic manipulations within operational constraints, adaptively re-optimizing beamline parameters and scan ranges in response to changes in the incoming beam. Measurements are streamed to the S3DF computing cluster, where we perform online, 6-dimensional phase-space reconstruction using generative techniques. This framework produces detailed 6-dimensional beam reconstructions at a rate of once every five minutes, enabling multi-hour tracking of injector beam evolution with unprecedented fidelity. These results represent a significant step toward routine, real-time 6-dimensional beam diagnostics for current and next-generation accelerator facilities.
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