Speaker
Description
We present an automated data reduction and analysis pipeline for X-ray reflectometry (XRR), designed for the LISA instrument at the P08 beamline at DESY. It is adapted from the development of solid surface XRR AI analysis \cite{1,2,3} to be used for liquid surfaces and interfaces. Using AI-based models, it enables real-time processing and analysis of reflectivity measurements. By providing immediate feedback during experiments, it enhances measurement efficiency and supports informed decision-making. As a Python package, this pipeline will also make advanced analysis accessible to less experienced users, improving reproducibility and usability.
In parallel, we present the PaN Reflectivity Database, a resource compiling published photon and neutron reflectometry metadata to serve as a reference for high-quality reflectometry datasets. To facilitate data contributions, we provide a Streamlit-based upload tool where users can enter metadata for their datasets. Before submission, data owners confirm publication under the Creative Commons Attribution (CC-BY) License to ensure open accessibility. Following an internal curation process, both metadata and datasets become publicly available in the database.
By integrating AI-driven automation with open data initiatives, our work advances the accessibility, transparency, and reproducibility of XRR data analysis.
