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X-ray data reconstruction from incomplete data sampling
What is it about?
The paper presents a method for reconstructing incomplete X-ray powder diffraction data, addressing cases where technical limitations—such as multi-detector setups—lead to missing information. This approach is particularly relevant for complex or disordered samples, where local structural order is of interest. A modified version of the Papoulis–Gerchberg algorithm is proposed, which iteratively estimates the missing data by enforcing known constraints, like the measured data points and certain frequency limits. The main improvements and added features are the use of an auto-regressive model as a starting point for the reconstruction algorithm and the definition of quantitative metrics to evaluate the reconstruction performance.
Why is it important?
The proposed method provides a reliable, robust solution for handling incomplete or gapped data collected during X-ray experiments, which otherwise severely limits certain types of advanced analysis. Furthermore, it has the potential to help researchers who need to calculate secondary data from their experimental results, and to advanced experimental setups where data collection is maximised for speed, even if it results in data gaps.