部分子密度理论的可解释人工智能分类
Explainable AI classification for parton density theory
AI总结：
本文提出可解释AI框架XAI4PDF，利用类ResNet网络和引导反向传播对部分子分布函数按味或理论模型分类，并定位受理论假设影响最大的x区域。
摘要：
定量地将部分子分布函数（PDF，即部分子密度）的性质与产生它们的QCD分析中所作的理论假设联系起来，一直是高能物理唯象学中的一个长期难题。为应对这一挑战，我们引入了一个基于机器学习的可解释性框架$\ exttt{XAI4PDF}$，利用类ResNet神经网络（NN）按部分子味或底层理论模型对PDF进行分类。通过利用ResNet模型的可微性质，该方法采用引导反向传播来剖析拟合PDF的相关特征，识别对机器学习模型分类重要的PDF的x依赖特征。通过应用我们的框架，我们能够根据产生PDF的分析对其进行分类，同时构建定量的、人类可读的图谱，定位受各分析内部理论假设影响最大的x区域。该技术扩展了PDF分析及相关粒子唯象学可用的工具集，并指向有前景的推广方向。
英文摘要：
Quantitatively connecting properties of parton distribution functions (PDFs, or parton densities) to the theoretical assumptions made within the QCD analyses which produce them has been a longstanding problem in HEP phenomenology. To confront this challenge, we introduce an ML-based explainability framework, $\texttt{XAI4PDF}$, to classify PDFs by parton flavor or underlying theoretical model using ResNet-like neural networks (NNs). By leveraging the differentiable nature of ResNet models, this approach deploys guided backpropagation to dissect relevant features of fitted PDFs, identifying x-dependent signatures of PDFs important to the ML model classifications. By applying our framework, we are able to sort PDFs according to the analysis which produced them while constructing quantitative, human-readable maps locating the x regions most affected by the internal theory assumptions going into each analysis. This technique expands the toolkit available to PDF analysis and adjacent particle phenomenology while pointing to promising generalizations.
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