Orateur
Sabine Kraml
(LPSC Grenoble, CNRS/IN2P3)
Description
With the increasing usage of machine learning in high energy physics analyses, the publication of the learned models in a reusable form has become a crucial question for analysis preservation and reuse. In turn, a lack of appropriate ML design and publication makes reinterpretation of analyses in terms of physics scenarios beyond those considered in the original experimental paper seriously difficult if not impossible. I will discuss recent efforts towards the preservation and reuse of ML-based LHC analyses together with guidelines for reusable ML models, which originated from the LHC Reinterpretation Forum and the 2023 PhysTeV workshop in Les Houches.
Auteur principal
Sabine Kraml
(LPSC Grenoble, CNRS/IN2P3)
