eLog analysis for accelerators: status and future outlook

This work demonstrates electronic logbook (eLog) systems leveraging modern AI-driven information retrieval capabilities at the accelerator facilities of Fermilab, Jefferson Lab, Lawrence Berkeley National Laboratory (LBNL), SLAC National Accelerator Laboratory. We evaluate contemporary tools and methodologies for information retrieval with Retrieval Augmented Generation (RAGs), focusing on operational insights and integration with existing accelerator control systems.The study addresses challenges and proposes solutions for state-of-the-art eLog analysis through practical implementations, demonstrating applications and limitations. We present a framework for enhancing accelerator facility operations through improved information accessibility and knowledge management, which could potentially lead to more efficient operations.

@article{sulc2025_2506.12949,
  title={ eLog analysis for accelerators: status and future outlook },
  author={ Antonin Sulc and Thorsten Hellert and Aaron Reed and Adam Carpenter and Alex Bien and Chris Tennant and Claudio Bisegni and Daniel Lersch and Daniel Ratner and David Lawrence and Diana McSpadden and Hayden Hoschouer and Jason St. John and Thomas Britton },
  journal={arXiv preprint arXiv:2506.12949},
  year={ 2025 }
}