Treece Bradley W, Kienzle Paul A, Hoogerheide David P, Majkrzak Charles F, Lösche Mathias, Heinrich Frank
Department of Physics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, USA.
Center for Neutron Research, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899-6102, USA.
J Appl Crystallogr. 2019 Feb 1;52(Pt 1):47-59. doi: 10.1107/S1600576718017016.
A framework based on Bayesian statistics and information theory is developed to optimize the design of surface-sensitive reflectometry experiments. The method applies to model-based reflectivity data analysis, uses simulated reflectivity data and is capable of optimizing experiments that probe a sample under more than one condition. After presentation of the underlying theory and its implementation, the framework is applied to exemplary test problems for which the information gain Δ is determined. Reflectivity data are simulated for the current generation of neutron reflectometers at the NIST Center for Neutron Research. However, the simulation can be easily modified for X-ray or neutron instruments at any source. With application to structural biology in mind, this work explores the dependence of Δ on the scattering length density of aqueous solutions in which the sample structure is bathed, on the counting time and on the maximum momentum transfer of the measurement. Finally, the impact of a buried magnetic reference layer on Δ is investigated.
开发了一种基于贝叶斯统计和信息论的框架，以优化表面敏感反射测量实验的设计。该方法适用于基于模型的反射率数据分析，使用模拟反射率数据，并且能够优化在多种条件下探测样品的实验。在介绍了基础理论及其实现之后，将该框架应用于确定信息增益Δ的示例性测试问题。针对美国国家标准与技术研究院中子研究中心当前一代中子反射仪模拟了反射率数据。然而，该模拟可以很容易地针对任何源的X射线或中子仪器进行修改。考虑到其在结构生物学中的应用，这项工作探讨了Δ对样品结构所处水溶液的散射长度密度、计数时间以及测量的最大动量转移的依赖性。最后，研究了掩埋的磁性参考层对Δ的影响。
