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 phase space characterization during routine operation demands reliable tomographic diagnostic measurements and fast, efficient reconstruction methods. In this work, we demonstrate the first fully autonomous 6-dimensional beam-tomography system deployed on the DIAG0 parasitic beamline at LCLS-II. Using machine-learning-based control algorithms, the system autonomously configures DIAG0 and executes tomographic manipulations within operational constraints, adaptively re-optimizing beamline parameters and scan ranges in response to changes in the incoming beam. Tomographic measurements are streamed to the S3DF computing cluster where generative analysis methods reconstruct the phase-space distribution. We demonstrate that this framework produces detailed 6-dimensional beam reconstructions at a cadence of one reconstruction every 5 to 10 minutes, enabling real-time, multi-hour monitoring of injector beam evolution with unprecedented fidelity. These results represent a significant step toward fully autonomous operation of accelerator beamlines with real-time beam diagnostics for current and next-generation accelerator facilities.
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