Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology
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.
Versions
Related
- The Goldilocks Prior: Physically Consistent Inference of Neutron Star Properties from Gravitational Wave Observations
- A generative diffusion framework for physically consistent 3D turbulence
- PINN-LPCDD: Physics-Informed Neural Network with Learnable and Physically Consistent Domain Decomposition for Compressible Multi-Medium Flows
- Physically Consistent 2D to 3D Pore Space Synthesis: 1. Dynamic Grain Packing Under Strict Morphology Constraints
- Information content of the Gamow Teller Resonance: Bayesian inference with self-consistent Skyrme models
BibTeX
@misc{alda2026physically,
title = {{Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology}},
author = {Jorge Alda and Jacobo Asorey and Alejandro Mir and Siannah Peñaranda},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.12726},
eprint = {2607.12726},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.12726},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology
AU - Jorge Alda
AU - Jacobo Asorey
AU - Alejandro Mir
AU - Siannah Peñaranda
PY - 2026
DA - 2026/09/08
PB - arXiv
DO - 10.48550/arxiv.2607.12726
UR - https://arxiv.org/abs/2607.12726
AB - 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.
ER -