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URLhttps://doi.org/10.18429/JACoW-IPAC2024-THPR37
TitleTowards unlocking insights from logbooks using AI
Authors
  • A. Sulc
    Helmholtz-Zentrum Berlin fuer Materialien und Energie GmbH
  • A. Bien, D. Ratner, W. Hu
    SLAC National Accelerator Laboratory
  • A. Eichler, F. Mayet, H. Tuennermann, J. Kaiser, R. Kammering, T. Wilksen
    Deutsches Elektronen-Synchrotron
  • F. Rehm, V. Kain
    European Organization for Nuclear Research
  • G. Hartmann
    Helmholtz-Zentrum Berlin für Materialien und Energie GmbH
  • H. Hoschouer, J. St. John, K. Hazelwood
    Fermi National Accelerator Laboratory
  • J. Maldonado
    Brookhaven National Laboratory
  • T. Hellert
    Lawrence Berkeley National Laboratory
AbstractElectronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of logbook entries can hinder their usability and automation. As natural language processing (NLP) continues advancing, it offers opportunities to address various challenges that logbooks present. This work explores jointly testing a tailored Retrieval Augmented Generation (RAG) model for enhancing the usability of particle accelerator logbooks at institutes like DESY, BESSY, Fermilab, BNL, SLAC, LBNL, and CERN. The RAG model uses a corpus built on logbook contributions and aims to unlock insights from these logbooks by leveraging retrieval over facility datasets, including discussion about potential multimodal sources. Our goals are to increase the FAIR-ness (findability, accessibility, interoperability, and reusability) of logbooks by exploiting their information content to streamline everyday use, enable macro-analysis for root cause analysis, and facilitate problem-solving automation.
Paperdownload: THPR37.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference15th International Particle Accelerator Conference
Series
LocationNashville, TN
Date19-24 May 2024
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardFulvia Pilat - Oak Ridge National Laboratory Wolfram Fischer - Brookhaven National Laboratory Robert Saethre - Oak Ridge National Laboratory Petr Anisimov - Los Alamos National Laboratory Ivan Andrian - Elettra-Sincrotrone Trieste S.C.p.A.
Online ISBN978-3-95450-247-9
Online ISSN2673-5490
Received16 May 2024
Revised16 May 2024
Accepted16 May 2024
Issued01 July 2024
DOI10.18429/JACoW-IPAC2024-THPR37
Pages3579-3582