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Les Houches guide to reusable ML models in LHC analyses
by Jack Y. Araz, Andy Buckley, Gregor Kasieczka, Jan Kieseler, Sabine Kraml, Anders Kvellestad, Andre Lessa, Tomasz Procter, Are Raklev, Humberto Reyes-Gonzalez, Krzysztof Rolbiecki, Sezen Sekmen, Gokhan Unel
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Submission summary
| Authors (as registered SciPost users): | Jack Araz · Andy Buckley · Sabine Kraml · Andre Lessa · Tomasz Procter · Krzysztof Rolbiecki · Sezen Sekmen |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2312.14575v3 (pdf) |
| Date accepted: | 2024-10-08 |
| Date submitted: | 2024-09-17 23:24 |
| Submitted by: | Procter, Tomasz |
| Submitted to: | SciPost Physics Community Reports |
| Ontological classification | |
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| Academic field: | Physics |
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Abstract
With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse. The complexity of these models creates practical issues for both reporting them accurately and for ensuring the stability of their behaviours in different environments and over extended timescales. In this note we discuss the current state of affairs, highlighting specific practical issues and focusing on the most promising technical and strategic approaches to ensure trustworthy analysis-preservation. This material originated from discussions in the LHC Reinterpretation Forum and the 2023 PhysTeV workshop at Les Houches.
Published as SciPost Phys. Comm. Rep. 3 (2024)