Larsen Andreas Haahr
University of Copenhagen Niels Bohr Institute Universitetsparken 5 2100 Copenhagen Denmark.
J Appl Crystallogr. 2025 May 2;58(Pt 3):934-947. doi: 10.1107/S1600576725002390. eCollection 2025 Jun 1.
Small-angle X-ray and neutron scattering (SAXS and SANS) are powerful techniques in materials science and soft matter. This study addressed how multiple SAXS or SANS datasets are best weighted when performing simultaneous fitting. Three weighting schemes were tested: (1) equal weighting of all datapoints, (2) equal weighting of each dataset through normalization with the number of datapoints and (3) weighting proportional to the information content. The weighting schemes were assessed by model refinement against synthetic data under numerous conditions. The first weighting scheme led to the most accurate parameter estimation, especially when one dataset substantially outnumbered the other(s). Furthermore, it was demonstrated that inclusion of Gaussian priors significantly improves the accuracy of the refined parameters, as compared with common practice, where each parameter is constrained uniformly within an allowed interval.
小角X射线和中子散射（SAXS和SANS）是材料科学和软物质领域的强大技术。本研究探讨了在进行同时拟合时，如何对多个SAXS或SANS数据集进行最佳加权。测试了三种加权方案：（1）所有数据点的等权重，（2）通过用数据点数量进行归一化对每个数据集进行等权重，以及（3）与信息含量成比例的加权。通过在众多条件下对合成数据进行模型精修来评估加权方案。第一种加权方案导致了最准确的参数估计，特别是当一个数据集的数据点数量大大超过其他数据集时。此外，结果表明，与通常将每个参数在允许区间内均匀约束的做法相比，纳入高斯先验显著提高了精修参数的准确性。
