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Towards Unlocking Insights from Logbooks Using AI

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arxiv 2406.12881 v1 pith:EXY3L4PG submitted 2024-05-25 physics.acc-ph cs.CL

Antonin Sulc , Alex Bien , Annika Eichler , Daniel Ratner , Florian Rehm , Frank Mayet , Gregor Hartmann , Hayden Hoschouer
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Henrik Tuennermann Jan Kaiser Jason St. John Jennefer Maldonado Kyle Hazelwood Raimund Kammering Thorsten Hellert Tim Wilksen Verena Kain Wan-Lin Hu
This is my paper · ORCID
classification physics.acc-phcs.CL
keywords logbooksacceleratorautomationinformationinsightslogbookmodelparticle
verification ladder T0 review T1 audit T2 compute T3 formal
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Electronic 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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility

    physics.acc-ph 2026-07 conditional novelty 5.5 of 10

    A deployed hybrid RAG for APS operations improves vital-nugget recall over BM25 mainly via cross-encoder reranking; graph and corrective loops help only marginally on a 50-question facility benchmark.

  2. Application Of Large Language Models For The Extraction Of Information From Particle Accelerator Technical Documentation

    cs.IR 2025-09 conditional novelty 4.0 of 10

    A RAG pipeline for accelerator documentation works best with 800-character chunks and top-5 retrieval, and translating German documents helps retrieval.

  3. eLog analysis for accelerators: status and future outlook

    hep-ex 2025-06 conditional novelty 4.0 of 10

    A status report on RAG-based eLog search implementations at four accelerator facilities, with system descriptions but no quantitative evaluation.

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