TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCommRep.3
TI  - Les Houches guide to reusable ML models in LHC analyses
PY  - 2024/11/06
UR  - https://scipost.org/SciPostPhysCommRep.3
JF  - SciPost Physics Community Reports
JA  - SciPost Phys. Comm. Rep.
SP  - 3
A1  - Araz, Jack Y.
AU  - Buckley, Andy
AU  - Kasieczka, Gregor
AU  - Kieseler, Jan
AU  - Kraml, Sabine
AU  - Kvellestad, Anders
AU  - Lessa, Andre
AU  - Procter, Tomasz
AU  - Raklev, Are
AU  - Reyes-González, Humberto
AU  - Rolbiecki, Krzysztof
AU  - Sekmen, Sezen
AU  - Ünel, Gokhan
AB  - 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.
ER  -

@Article{10.21468/SciPostPhysCommRep.3,
	title={{Les Houches guide to reusable ML models in LHC analyses}},
	author={Jack Y. Araz and Andy Buckley and Gregor Kasieczka and Jan Kieseler and Sabine Kraml and Anders Kvellestad and Andre Lessa and Tomasz Procter and Are Raklev and Humberto Reyes-Gonzalez and Krzysztof Rolbiecki and Sezen Sekmen and Gokhan Unel},
	journal={SciPost Phys. Comm. Rep.},
	pages={3},
	year={2024},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCommRep.3},
	url={https://scipost.org/10.21468/SciPostPhysCommRep.3},
}