Parameter inversion of a polydisperse system in small‐angle scattering. Issue 4 (2nd August 2022)

• Record Type: Journal Article
• Authors: Leng, Kuangdai; King, Stephen; Snow, Tim; Rogers, Sarah; Markvardsen, Anders; Maheswaran, Satheesh; Thiyagalingam, Jeyan
• Is Part Of: Journal of applied crystallography. Volume 55:Issue 4(2022)
• Page Start: 966
• Page End: 977
• Publication Date: 2022-08-02
• Subjects: small‐angle scattering -- polydispersity -- inversion -- neutron scattering -- X‐ray scattering -- nonlinear programming; Crystallography -- Periodicals; 548.05
• DOI: 10.1107/S1600576722006379
• Languages: English
• ISSNs: 0021-8898

Abstract

Abstract : An accurate and efficient method for model‐ and form‐free inversion of a polydisperse small‐angle scattering system is presented. It supports an arbitrary number of model parameters and both 1D and 2D intensity observations. Abstract : A general method to invert parameter distributions of a polydisperse system using data acquired from a small‐angle scattering (SAS) experiment is presented. The forward problem, i.e. calculating the scattering intensity given the distributions of any causal parameters of a theoretical model, is generalized as a multi‐linear map, characterized by a high‐dimensional Green tensor that represents the complete scattering physics. The inverse problem, i.e. finding the maximum‐likelihood estimation of the parameter distributions (in free form) given the scattering intensity (either a curve or an image) acquired from an experiment, is formulated as a constrained nonlinear programming (NLP) problem. This NLP problem is solved with high accuracy and efficiency via several theoretical and computational enhancements, such as an automatic data scaling for accuracy preservation and GPU acceleration for large‐scale multi‐parameter systems. Six numerical examples are presented, including both synthetic tests and solutions to real neutron and X‐ray data sets, where the method is compared with several existing methods in terms of their generality, accuracy and computational cost. These examples show that SAS inversion is subject to a high degree of non‐uniqueness of solution or structural ambiguity. With an ultra‐high accuracy, the method can yield a series of near‐optimal solutions that fit data to different acceptable levels.