TY - JOUR

T1 - Experimental noise in small-angle scattering can be assessed using the Bayesian indirect Fourier transformation

AU - Larsen, Andreas Haahr

AU - Pedersen, Martin Cramer

PY - 2021/10/1

Y1 - 2021/10/1

N2 - 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 data sets used to refine the same model. Therefore, it is problematic when experimental errors are over- or underestimated. A method is presented, using Bayesian indirect Fourier transformation for small-angle scattering data, to assess whether or not a given small-angle scattering data set has over- or underestimated experimental errors. The method is effective on both simulated and experimental data, and can be used to 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 chi(2) 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 small-angle scattering data or ab initio modelling. Using the outlined method, it is shown that one can determine what reduced chi(2) to aim for when fitting a model against small-angle scattering data. The method is easily accessible via the web interface BayesApp.

AB - 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 data sets used to refine the same model. Therefore, it is problematic when experimental errors are over- or underestimated. A method is presented, using Bayesian indirect Fourier transformation for small-angle scattering data, to assess whether or not a given small-angle scattering data set has over- or underestimated experimental errors. The method is effective on both simulated and experimental data, and can be used to 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 chi(2) 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 small-angle scattering data or ab initio modelling. Using the outlined method, it is shown that one can determine what reduced chi(2) to aim for when fitting a model against small-angle scattering data. The method is easily accessible via the web interface BayesApp.

KW - small-angle scattering

KW - BIFT

KW - Bayesian indirect Fourier transformation

KW - experimental noise

KW - model refinement

KW - X-RAY-SCATTERING

KW - MACROMOLECULES

KW - REFINEMENT

KW - ERRORS

U2 - 10.1107/S1600576721006877

DO - 10.1107/S1600576721006877

M3 - Journal article

VL - 54

SP - 1281

EP - 1289

JO - Journal of Applied Crystallography

JF - Journal of Applied Crystallography

SN - 0021-8898

ER -