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  • Open Access

Machine-state embeddings as an operational reference space for accelerator operation

Chris Tennant*

Jundong Li and Song Wang

  • Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, Virginia 22903, USA

  • *Contact author: tennant@jlab.org
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Phys. Rev. Accel. Beams 29, 105101 – Published 1 October, 2026

DOI: https://doi.org/10.1103/d1b7-qdcm

Abstract

We demonstrate that graph neural network embeddings of injector configurations provide a practical operational reference space for the Continuous Electron Beam Accelerator Facility injector at Jefferson Lab. Using 137,389 snapshots spanning January 2022 through March 2023, we show that injector operation occupies a small number of persistent, well-separated neighborhoods in a 16-dimensional learned state space rather than a featureless continuum. Density-based clustering identifies ten persistent operating regimes with strong operational run alignment, and regime persistence statistics confirm that these regimes are stable over timescales of hours to weeks. Large relocations between neighborhoods are rare and episodic; 99.6% of 1-h operating windows fall within an empirically derived jitter baseline. Geometric outlier screening narrows a year-long dataset to a small set of intervals warranting operational review, and nearest-neighbor retrieval enables case-based reasoning over the historical archive. A controlled beam study validates that deliberate injector reconfiguration traces coherent, interpretable trajectories in embedding space. Together these capabilities demonstrate that machine-state embeddings support holistic operational monitoring in ways that single-channel inspection cannot.

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Phys. Rev. Accel. Beams 29, 105101– Published 1 October, 2026
  • Received 7 April 2026
  • Accepted 4 September 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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