物理一致的参数推断：高能物理和宇宙学中的透明机器学习模拟
Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology
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中文总结 AI 辅助
高能物理和宇宙学全局拟合面临挑战，本文用梯度提升回归树构建机器学习框架模拟复杂似然景观，讨论其优势，通过应用于味异常分析等验证，利用SHAP值确保预测物理可解释且与基础物理一致。
中文摘要 AI 辅助
在高能物理和宇宙学中，全局拟合常面临挑战，即使用计算昂贵或拓扑复杂的似然函数来探索高维参数空间。本文提出一个机器学习框架，旨在用梯度提升回归树（XGBoost）模拟复杂且常为非高斯的似然景观。讨论了机器学习方法在计算效率和置信区域分辨率方面的优势，特别是在具有复杂相关性或“弯曲”简并性的场景中。通过将其应用于近期对半轻子B介子衰变中味异常的分析，并讨论该框架对其他现象学系统（如类轴子粒子或宇宙学全局拟合）的适应性来验证此方法。最后，利用SHAP值对特征重要性进行透明分析，确保机器学习预测在物理上可解释且与基础物理一致。
英文摘要
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.
发表机构
- Centro de Astropartículas y Física de Altas Energías (CAPA)（高能物理与天体粒子研究中心）
- Departamento de Física Teórica, Facultad de Ciencias, Universidad de Zaragoza（萨拉戈塔大学理论物理系）
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