  Small-angle X-ray and neutron scattering (SAXS and SANS) are powerful
techniques for elucidating the structure of diverse particles and materials.
This study address the challenge of effectively combining SAXS and SANS data
for accurate structural parameter determination. Surprisingly, our results
demonstrate that equally weighting all data points leads to the most accurate
parameter estimation, even when SAXS data significantly outnumber SANS data. We
compared this approach with weighting schemes normalized by the number of
points and by the derived information content. Furthermore, we assessed the
impact of prior knowledge by incorporating Gaussian priors for model
parameters. Our findings indicate that Gaussian priors improve the accuracy of
refined parameter values compared to uniform priors. When using a minimum and a
maximum values for model parameters, which is common practice, uniform priors
are implicitely applied. Finally, we show that utilizing information content
aids in determining the degrees of freedom, enabling accurate calculation of
the goodness of fit. In conclusion, this research provides valuable insights
into the optimal combination of SAXS and SANS data, emphasizing the importance
of weighting schemes and prior knowledge for enhanced accuracy in structural
parameter determination.
