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000607019 0247_ $$2arXiv$$aarXiv:2406.12881
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000607019 041__ $$aEnglish
000607019 088__ $$2arXiv$$aarXiv:2406.12881
000607019 088__ $$2Fermilab$$aFERMILAB-CONF-24-0237-AD
000607019 088__ $$2arXiv$$aarXiv:2406.12881
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000607019 1001_ $$0P:(DE-HGF)0$$aSulc, Antonin$$b0$$eCorresponding author
000607019 1112_ $$a15th International Particle Accelerator Conference$$cNashville$$d2024-05-19 - 2024-05-24$$gIPAC'24$$wUSA
000607019 245__ $$aTowards unlocking insights from logbooks using AI
000607019 260__ $$aGeneva, Switzerland$$bJACoW Publishing$$c2024
000607019 29510 $$a[Ebook] 15th International Particle Accelerator Conference, Nashville, Tennessee : May 19-24, 2024, Nashville, Tennessee, USA : proceedings / Pilat, Fulvia ; Andrian, Ivan , [Geneva] : JACoW Publishing, [2024],
000607019 300__ $$a3579 - 3582
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000607019 500__ $$a5 pages, 1 figure, 15th International Particle Accelerator Conference
000607019 520__ $$aElectronic 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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000607019 536__ $$0G:(DE-HGF)2020_InternLabs-0011$$aInternLabs-0011 - HIR3X - Helmholtz International Laboratory on Reliability, Repetition, Results at the most advanced X-ray Sources (2020_InternLabs-0011)$$c2020_InternLabs-0011$$x1
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000607019 650_7 $$2Other$$aAccelerator Physics
000607019 650_7 $$2Other$$amc8-application-of-accelerators-technology-transfer-industrial-relations-and-outreach - MC8: Application of Accelerators, Technology Transfer, Industrial Relations, and Outreach
000607019 650_7 $$2Other$$aMC8.U09 - MC8.U09 Other Applications
000607019 650_7 $$2autogen$$aacceleration
000607019 650_7 $$2autogen$$aoperation
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000607019 650_7 $$2autogen$$aembedded
000607019 650_7 $$2autogen$$aelectron
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000607019 650_7 $$2autogen$$atiming
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000607019 7001_ $$aBien, Alex$$b1
000607019 7001_ $$0P:(DE-H253)PIP1087213$$aEichler, Annika$$b2$$udesy
000607019 7001_ $$aRatner, Daniel$$b3
000607019 7001_ $$aRehm, Florian$$b4
000607019 7001_ $$0P:(DE-H253)PIP1014786$$aMayet, Frank$$b5$$udesy
000607019 7001_ $$aHartmann, Gregor$$b6
000607019 7001_ $$aHoschouer, Hayden$$b7
000607019 7001_ $$0P:(DE-H253)PIP1088102$$aTuennermann, Henrik$$b8$$udesy
000607019 7001_ $$aKaiser, Jan$$b9
000607019 7001_ $$aSt. John, Jason$$b10
000607019 7001_ $$aMaldonado, Jennefer$$b11
000607019 7001_ $$aHazelwood, K. J.$$b12
000607019 7001_ $$0P:(DE-H253)PIP1002321$$aKammering, Raimund$$b13$$udesy
000607019 7001_ $$0P:(DE-H253)PIP1013281$$aHellert, Thorsten$$b14$$udesy
000607019 7001_ $$0P:(DE-H253)PIP1007238$$aWilksen, Tim$$b15$$udesy
000607019 7001_ $$aKain, Verena$$b16
000607019 7001_ $$aHu, Wan-Lin$$b17
000607019 773__ $$a10.18429/JACoW-IPAC2024-THPR37
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