Optimal weights and priors in simultaneous fitting of multiple small-angle scattering datasets

Small-angle X-ray and neutron scattering (SAXS and SANS) are powerful techniques in materials science and soft matter. This study addressed how multiple SAXS or SANS datasets are best weighted when performing simultaneous fitting. Three weighting schemes were tested: (1) equal weighting of all datapoints, (2) equal weighting of each dataset through normalization with the number of datapoints and (3) weighting proportional to the information content. The weighting schemes were assessed by model refinement against synthetic data under numerous conditions. The first weighting scheme led to the most accurate parameter estimation, especially when one dataset substantially outnumbered the other(s). Furthermore, it was demonstrated that inclusion of Gaussian priors significantly improves the accuracy of the refined parameters, as compared with common practice, where each parameter is constrained uniformly within an allowed interval.