We describe a neural network-based tool for the automatic estimation of thin film thicknesses and scattering length densities from neutron reflectivity curves. The neural network sits within a data pipeline, that takes raw data from a neutron reflectometer, and outputs data and parameter estimates into a fitting program for end user analysis. Our tool deals with simple cases, predicting the number of layers and layer parameters up to three layers on a bulk substrate. This provides good accuracy in parameter estimation, while covering a large portion of the use case. By automating steps in data analysis that only require semi-expert knowledge, we lower the barrier to on-experiment data analysis, allowing better utility to be made from large scale facility experiments. Transfer learning showed that our tool works for x-ray reflectivity, and all code is freely available on GitHub (neutron-net 2020, available at: https://github.com/xmironov/neutron-net) (Accessed: 25 June 2020).

我们描述了一种基于神经网络的工具，用于根据中子反射率曲线自动估计薄膜厚度和散射长度密度。神经网络位于数据管道中，从中子反射计获取原始数据，并将数据和参数估计输出到拟合程序中以供最终用户分析。我们的工具处理简单的情况，预测大块基板上最多三层的层数和层参数。这在参数估计方面提供了良好的准确性，同时涵盖了大部分用例。通过自动化只需要半专业知识的数据分析步骤，我们降低了进行实验数据分析的障碍，从而使大规模设施实验具有更好的效用。迁移学习表明我们的工具适用于 X 射线反射率，