Obayashi Ippei, Miyajima Shinya, Tanaka Kazuaki, Mayumi Koichi
Center for Artificial Intelligence and Mathematical Data Science, Okayama University, Japan.
Faculty of Science and Engineering, Iwate University, Japan.
J Appl Crystallogr. 2025 May 31;58(Pt 3):976-991. doi: 10.1107/S1600576725003334. eCollection 2025 Jun 1.
Contrast variation small-angle neutron scattering (CV-SANS) is a powerful tool for evaluating the structure of multi-component systems. In CV-SANS, the scattering intensities () measured with different scattering contrasts are de-com-posed into partial scattering functions () of the self- and cross-correlations between components. Since the measurement has a measurement error, () must be estimated statistically from (). If no prior knowledge about () is available, the least-squares method is best, and this is the most popular estimation method. However, if prior knowledge is available, the estimation can be improved using Bayesian inference in a statistically authorized way. In this paper, we propose a novel method to improve the estimation of (), based on Gaussian process regression using prior knowledge about the smoothness and flatness of (). We demonstrate the method using synthetic core-shell and experimental polyrotaxane SANS data.
对比变化小角中子散射（CV-SANS）是评估多组分系统结构的强大工具。在CV-SANS中，用不同散射对比度测量的散射强度（）被分解为组分之间自相关和互相关的部分散射函数（）。由于测量存在测量误差，（）必须从（）进行统计估计。如果没有关于（）的先验知识，最小二乘法是最好的，这也是最流行的估计方法。然而，如果有先验知识可用，则可以使用贝叶斯推理以统计认可的方式改进估计。在本文中，我们基于利用关于（）的平滑度和平面度的先验知识的高斯过程回归，提出了一种改进（）估计的新方法。我们使用合成核壳和实验聚轮烷SANS数据演示了该方法。
