scholarly journals SASfit: a tool for small-angle scattering data analysis using a library of analytical expressions

2015 ◽  
Vol 48 (5) ◽  
pp. 1587-1598 ◽  
Author(s):  
Ingo Breßler ◽  
Joachim Kohlbrecher ◽  
Andreas F. Thünemann
Keyword(s):  
Data Analysis ◽  
Reference Material ◽  
Small Angle ◽  
Fractal Model ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
Model Parameters ◽  
Angle Scattering ◽  
Confidence Assessment ◽  
Analysis Workflow

SASfitis one of the mature programs for small-angle scattering data analysis and has been available for many years. This article describes the basic data processing and analysis workflow along with recent developments in theSASfitprogram package (version 0.94.6). They include (i) advanced algorithms for reduction of oversampled data sets, (ii) improved confidence assessment in the optimized model parameters and (iii) a flexible plug-in system for custom user-provided models. A scattering function of a mass fractal model of branched polymers in solution is provided as an example for implementing a plug-in. The newSASfitrelease is available for major platforms such as Windows, Linux and MacOS. To facilitate usage, it includes comprehensive indexed documentation as well as a web-based wiki for peer collaboration and online videos demonstrating basic usage. The use ofSASfitis illustrated by interpretation of the small-angle X-ray scattering curves of monomodal gold nanoparticles (NIST reference material 8011) and bimodal silica nanoparticles (EU reference material ERM-FD-102).

scholarly journals Small Angle Scattering Data Analysis Assisted by Machine Learning Methods

MRS Advances ◽  
2020 ◽  
Vol 5 (29-30) ◽  
pp. 1577-1584
Author(s):  
Changwoo Do ◽  
Wei-Ren Chen ◽  
Sangkeun Lee
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Small Angle ◽  
Confusion Matrix ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
Search Spaces ◽  
Angle Scattering ◽  
Analysis Workflow ◽  
The Right

ABSTRACTSmall angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with poses a big challenge for beginners to SAS data analysis. Here, we present machine learning (ML) methods that can assist users by suggesting scattering models for data analysis. A series of one-dimensional scattering curves have been generated by using different models to train the algorithms. The performance of the ML method is studied for various types of ML algorithms, resolution of the dataset, and the number of the dataset. The degree of similarities among selected scattering models is presented in terms of the confusion matrix. The scattering model suggestions with prediction scores provide a list of scattering models that are likely to succeed. Therefore, if implemented with extensive libraries of scattering models, this method can speed up the data analysis workflow by reducing search spaces for appropriate scattering models.

Small-angle scattering data analysis for dense polydisperse systems: the FLAC program

2000 ◽  
Vol 133 (1) ◽  
pp. 66-75 ◽  
Author(s):  
Flavio Carsughi ◽  
Achille Giacometti ◽  
Domenico Gazzillo
Keyword(s):  
Data Analysis ◽  
Small Angle ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
Angle Scattering

scholarly journals ATSAS 2.1 – towards automated and web-supported small-angle scattering data analysis

2007 ◽  
Vol 40 (s1) ◽  
pp. s223-s228 ◽  
Author(s):  
Maxim V. Petoukhov ◽  
Peter V. Konarev ◽  
Alexey G. Kikhney ◽  
Dmitri I. Svergun
Keyword(s):  
Data Analysis ◽  
Small Angle ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
Angle Scattering

scholarly journals Analysis of small-angle scattering data using model fitting and Bayesian regularization

2018 ◽  
Vol 51 (4) ◽  
pp. 1151-1161 ◽  
Author(s):  
Andreas Haahr Larsen ◽  
Lise Arleth ◽  
Steen Hansen
Keyword(s):  
Prior Knowledge ◽  
Small Angle ◽  
Regularization Method ◽  
Prior Information ◽  
Form Factors ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
Model Parameters ◽  
Bayesian Regularization ◽  
Angle Scattering

The structure of macromolecules can be studied by small-angle scattering (SAS), but as this is an ill-posed problem, prior knowledge about the sample must be included in the analysis. Regularization methods are used for this purpose, as already implemented in indirect Fourier transformation and bead-modeling-based analysis of SAS data, but not yet in the analysis of SAS data with analytical form factors. To fill this gap, a Bayesian regularization method was implemented, where the prior information was quantified as probability distributions for the model parameters and included via a functional S. The quantity Q = χ2 + αS was then minimized and the value of the regularization parameter α determined by probability maximization. The method was tested on small-angle X-ray scattering data from a sample of nanodiscs and a sample of micelles. The parameters refined with the Bayesian regularization method were closer to the prior values as compared with conventional χ2 minimization. Moreover, the errors on the refined parameters were generally smaller, owing to the inclusion of prior information. The Bayesian method stabilized the refined values of the fitted model upon addition of noise and can thus be used to retrieve information from data with low signal-to-noise ratio without risk of overfitting. Finally, the method provides a measure for the information content in data, N g, which represents the effective number of retrievable parameters, taking into account the imposed prior knowledge as well as the noise level in data.

scholarly journals A robust expectation-maximization method for the interpretation of small-angle scattering data from dense nanoparticle samples

2019 ◽  
Vol 52 (5) ◽  
pp. 926-936
Author(s):  
M. Bakry ◽  
H. Haddar ◽  
O. Bunău
Keyword(s):  
Small Angle ◽  
Expectation Maximization ◽  
Scattering Problem ◽  
Inverse Scattering Problem ◽  
Small Angle Scattering ◽  
Fine Tuning ◽  
Scattering Data ◽  
Model Parameters ◽  
Dry Powders ◽  
Angle Scattering

The local monodisperse approximation (LMA) is a two-parameter model commonly employed for the retrieval of size distributions from the small-angle scattering (SAS) patterns obtained from dense nanoparticle samples (e.g. dry powders and concentrated solutions). This work features a novel implementation of the LMA model resolution for the inverse scattering problem. The method is based on the expectation-maximization iterative algorithm and is free of any fine-tuning of model parameters. The application of this method to SAS data acquired under laboratory conditions from dense nanoparticle samples is shown to provide good results.

Quantification of the information in small-angle scattering data

2014 ◽  
Vol 47 (6) ◽  
pp. 2000-2010 ◽  
Author(s):  
Martin Cramer Pedersen ◽  
Steen Laugesen Hansen ◽  
Bo Markussen ◽  
Lise Arleth ◽  
Kell Mortensen
Keyword(s):  
Data Analysis ◽  
Neutron Scattering ◽  
Ray Tracing ◽  
Information Content ◽  
Small Angle ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
X Ray ◽  
Angle Scattering ◽  
Wide Range

Small-angle X-ray and neutron scattering have become increasingly popular owing to improvements in instrumentation and developments in data analysis, sample handling and sample preparation. For some time, it has been suggested that a more systematic approach to the quantification of the information content in small-angle scattering data would allow for a more optimal experiment planning and a more reliable data analysis. In the present article, it is shown how ray-tracing techniques in combination with a statistically rigorous data analysis provide an appropriate platform for such a systematic quantification of the information content in scattering data. As examples of applications, it is shown how the exposure time at different instrumental settings or contrast situations can be optimally prioritized in an experiment. Also, the gain in information by combining small-angle X-ray and neutron scattering is assessed. While solution small-angle scattering data of proteins and protein–lipid complexes are used as examples in the present case study, the approach is generalizable to a wide range of other samples and experimental techniques. The source code for the algorithms and ray-tracing components developed for this study has been made available on-line.

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