  This work demonstrates the Python package mlreflect which implements an
optimized pipeline for the automized analysis of reflectometry data using
machine learning. The package combines several training and data treatment
techniques discussed in previous publications. The predictions made by the
neural network are accurate and robust enough to serve as good starting
parameters 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 thin
films on silicon substrates, the pipeline reliably finds an LMS minimum very
close to a fit produced by a human researcher with the application of physical
knowledge and carefully chosen boundary conditions. Furthermore, the
differences between simulated and experimental data and their implications for
the training and performance of neural networks are discussed. The experimental
test set is used to determine the optimal noise level during training.
Furthermore, the extremely fast prediction times of the neural network are
leveraged to compensate for systematic errors by sampling slight variations of
the data.
