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Small-angle X-ray and neutron scattering (SAXS and SANS) are powerfultechniques for elucidating the structure of diverse particles and materials.This study address the challenge of effectively combining SAXS and SANS datafor accurate structural parameter determination. Surprisingly, our resultsdemonstrate that equally weighting all data points leads to the most accurateparameter estimation, even when SAXS data significantly outnumber SANS data. Wecompared this approach with weighting schemes normalized by the number ofpoints and by the derived information content. Furthermore, we assessed theimpact of prior knowledge by incorporating Gaussian priors for modelparameters. Our findings indicate that Gaussian priors improve the accuracy ofrefined parameter values compared to uniform priors. When using a minimum and amaximum values for model parameters, which is common practice, uniform priorsare implicitely applied. Finally, we show that utilizing information contentaids in determining the degrees of freedom, enabling accurate calculation ofthe goodness of fit. In conclusion, this research provides valuable insightsinto the optimal combination of SAXS and SANS data, emphasizing the importanceof weighting schemes and prior knowledge for enhanced accuracy in structuralparameter determination.

Fitting multiple small-angle scattering datasets simultaneously: on the optimal use of priors and weights