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This work demonstrates the Python package mlreflect which implements anoptimized pipeline for the automized analysis of reflectometry data usingmachine learning. The package combines several training and data treatmenttechniques discussed in previous publications. The predictions made by theneural network are accurate and robust enough to serve as good startingparameters for an optional subsequent least mean squares (LMS) fit of the data.It is shown that for a large dataset of 242 reflectivity curves of various thinfilms on silicon substrates, the pipeline reliably finds an LMS minimum veryclose to a fit produced by a human researcher with the application of physicalknowledge and carefully chosen boundary conditions. Furthermore, thedifferences between simulated and experimental data and their implications forthe training and performance of neural networks are discussed. The experimentaltest set is used to determine the optimal noise level during training.Furthermore, the extremely fast prediction times of the neural network areleveraged to compensate for systematic errors by sampling slight variations ofthe data.

Neural network analysis of neutron and X-ray reflectivity data: automated analysis using mlreflect, experimental errors and feature engineering