  Magnetic nanoparticles offer a unique potential for various biomedical
applications, but prior to commercial usage a standardized characterization of
their structural and magnetic properties is required. For a thorough
characterization, the combination of conventional magnetometry and advanced
scattering techniques has shown great potential. In the present work, we
characterize a powder sample of high-quality iron oxide nanoparticles that are
surrounded with a homogeneous thick silica shell by DC magnetometry and
magnetic small-angle neutron scattering (SANS). To retrieve the particle
parameters such as their size distribution and saturation magnetization from
the data, we apply standard model fits of individual data sets as well as
global fits of multiple curves, including a combination of the magnetometry and
SANS measurements. We show that by combining a standard least-squares fit with
a subsequent Bayesian approach for the data refinement, the probability
distributions of the model parameters and their cross correlations can be
readily extracted, which enables a direct visual feedback regarding the quality
of the fit. This prevents an overfitting of data in case of highly correlated
parameters and renders the Bayesian method as an ideal component for a
standardized data analysis of magnetic nanoparticle samples.
