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This research applies gradient-boosted regression trees to emulate complex likelihood landscapes in high-energy physics, offering a computationally efficient alternative to traditional global fits. By integrating SHAP values for interpretability, the framework ensures that machine learning predictions remain physically consistent and transparent within phenomenological systems like flavour anomalies. The work demonstrates how ML can resolve topological complexities in parameter spaces without sacrificing adherence to underlying physical laws.
Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology
arXiv:2607.12726v2 Announce Type: replace-cross Abstract: Global fits in high energy physics and cosmology often face the challenge of exploring high-dimensional parameter spaces with computationally expensive or topologically complex likelihood functions. In this work, we present a Machine Learning framework designed to emulate complex, often non-Gaussian likelihood landscapes using gradient-boosted regression trees (XGBoost). We discuss the advantages of the Machine Learning approach in terms of computational efficiency and the resolution of confidence regions, particularly in scenarios with complex correlations or "curved" degeneracies. We validate this methodology by applying it to a recent analysis on flavour anomalies in semileptonic $B$ meson decays and discussing the adaptability of this framework to other phenomenological systems, such as axion-like particles or cosmology global fits. Finally, we utilise SHAP (Shapley Additive exPlanations) values to provide a transparent analysis of feature importance, ensuring that the Machine Learning predictions remain physically interpretable and consistent with the underlying physics.
Abstract:Global fits in high energy physics and cosmology often face the challenge of exploring high-dimensional parameter spaces with computationally expensive or topologically complex likelihood functions. In this work, we present a Machine Learning framework designed to emulate complex, often non-Gaussian likelihood landscapes using gradient-boosted regression trees (XGBoost). We discuss the advantages of the Machine Learning approach in terms of computational efficiency and the resolution of confidence regions, particularly in scenarios with complex correlations or "curved" degeneracies. We validate this methodology by applying it to a recent analysis on flavour anomalies in semileptonic $B$ meson decays and discussing the adaptability of this framework to other phenomenological systems, such as axion-like particles or cosmology global fits. Finally, we utilise SHAP (Shapley Additive exPlanations) values to provide a transparent analysis of feature importance, ensuring that the Machine Learning predictions remain physically interpretable and consistent with the underlying physics.
33 pages, 15 figures, 6 tables. Matches published version
High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG)
arXiv:2607.12726 [hep-ph]
(or arXiv:2607.12726v2 [hep-ph] for this version)
https://doi.org/10.48550/arXiv.2607.12726 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.3390/universe12080238 Focus to learn more DOI(s) linking to related resources
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