用机器学习高效模拟高能物理中的复杂参数空间，提升计算效率与结果可解释性。
Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology
- 用XGBoost模型拟合复杂的非高斯似然曲面，加速参数推断。
- 在夸克味异常分析中实现更快的置信区间计算，精度不降。
- 结合SHAP值揭示物理变量重要性，保证模型透明可解释。
高能物理与宇宙学中的全局拟合常面临高维参数空间及计算昂贵、拓扑复杂的似然函数挑战。本文提出一种基于梯度提升回归树（XGBoost）的机器学习框架，用于模拟复杂、通常非高斯的似然景观。该方法在计算效率和置信区域分辨率方面具有优势，尤其适用于存在复杂相关性或“弯曲退化”的情形。我们在半轻子B介子衰变中的味异常近期分析上验证了该方法，并探讨其在轴子样粒子或宇宙学全局拟合等其他现象学系统中的适应性。最后，利用SHAP（Shapley Additive exPlanations）值对特征重要性进行透明分析，确保机器学习预测与底层物理一致且可解释。
原文摘要 · Abstract (English)
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.
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