We introduce a Bayesian protocol based on artificial neural networks that is suitable for modeling inclusive electron-nucleus scattering on a variety of nuclear targets with quantified uncertainties. Unlike previous applications in the field, which directly parameterize the cross sections, our approach employs artificial neural networks to represent the longitudinal and transverse response functions. In contrast to cross sections, which depend on the incoming energy, scattering angle, and energy transfer, the response functions are determined solely by the energy and momentum transfer to the system, allowing the angular component to be treated analytically. We assess the accuracy and predictive power of our framework against the extensive data in the quasielastic inclusive electron-scattering database. Additionally, we present novel extractions of the longitudinal and transverse response functions and compare them with previous experimental analysis and nuclear ab-initio calculations.

中文翻译：

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使用贝叶斯人工神经网络对包容性电子核散射进行建模


我们引入了一种基于人工神经网络的贝叶斯协议，该协议适用于对具有量化不确定性的各种核目标上的包容性电子核散射进行建模。与该领域以前直接参数化横截面的应用不同，我们的方法采用人工神经网络来表示纵向和横向响应函数。与取决于入射能量、散射角和能量转移的横截面不同，响应函数完全由向系统的能量和动量传递决定，因此可以对角度分量进行解析处理。我们根据准弹性包容性电子散射数据库中的大量数据评估框架的准确性和预测能力。此外，我们提出了纵向和横向响应函数的新提取，并将它们与以前的实验分析和核 ab-initio 计算进行了比较。