Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology
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 validat
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- PossiblePossibly related (embedding) · 57%SpaceTimeKhantinuum/ml →
- PossiblePossibly related (embedding) · 56%arogozhnikov/hep_ml →
- PossiblePossibly related (embedding) · 53%NVIDIA/physicsnemo →
- PossiblePossibly related (embedding) · 50%Principled approaches for extending neural architectures to function spaces for operator learning →
- PossiblePossibly related (embedding) · 49%SciML/NeuralPDE.jl →
- PossiblePossibly related (embedding) · 47%luigibonati/mlcolvar →
- LinkedLinked via arxiv author · 85%Jorge Alda →
“Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology”
- LinkedLinked via arxiv author · 85%Jacobo Asorey →
“Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology”