1 research outputs found
Experimental noise in small-angle scattering can be assessed and corrected using the Bayesian Indirect Fourier Transformation
Small-angle X-ray and neutron scattering are widely used to investigate soft
matter and biophysical systems. The experimental errors are essential when
assessing how well a hypothesized model fits the data. Likewise, they are
important when weights are assigned to multiple datasets used to refine the
same model. Therefore, it is problematic when experimental errors are over- or
underestimated. We present a method, using Bayesian Indirect Fourier
Transformation for small-angle scattering data, to assess whether or not a
given small-angle scattering dataset has over- or underestimated experimental
errors. The method is effective on both simulated and experimental data, and
can be used assess and rescale the errors accordingly. Even if the estimated
experimental errors are appropriate, it is ambiguous whether or not a model
fits sufficiently well, as the "true" reduced of the data is not
necessarily unity. This is particularly relevant for approaches where
overfitting is an inherent challenge, such as reweighting of a simulated
molecular dynamics trajectory against a small-angle scattering data or ab
initio modelling. Using the outlined method, we show that one can determine
what reduced to aim for when fitting a model against small-angle
scattering data. The method is easily accessible via a web interface