Towards Unlocking Insights from Logbooks Using AI
Abstract
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.
Keywords
Cite
@article{arxiv.2406.12881,
title = {Towards Unlocking Insights from Logbooks Using AI},
author = {Antonin Sulc and Alex Bien and Annika Eichler and Daniel Ratner and Florian Rehm and Frank Mayet and Gregor Hartmann and Hayden Hoschouer and Henrik Tuennermann and Jan Kaiser and Jason St. John and Jennefer Maldonado and Kyle Hazelwood and Raimund Kammering and Thorsten Hellert and Tim Wilksen and Verena Kain and Wan-Lin Hu},
journal= {arXiv preprint arXiv:2406.12881},
year = {2024}
}
Comments
5 pages, 1 figure, 15th International Particle Accelerator Conference