Contemporary GiSAXS Data Analysis for I22

The synchrotron at Diamond Light Source facilitates access to a variety of techniques for analysing matter, ranging in complexity from ideal, model systems through to real-world applications. This broad spectrum of analytical techniques presents a unique challenge; the unification of many different, disparate, software packages to work together in unison providing our users with the information they desire, as quickly as possible.

I22 is one of Diamond’s experimental end stations, or beamlines, that has the capability of allowing our users to perform x-ray scattering experiments either by directing x-rays through a sample (transmission mode) or by reflecting x-rays off of samples at grazing, i.e. small, angles. These two distinct approaches to how x-rays interact with a sample, combined with a long sample to x-ray detection distance, allow I22 to collect Small Angle X-ray Scattering (SAXS) and Grazing incidence Small Angle X-ray Scattering (GiSAXS) datasets, respectively.

This project will focus on the analysis of data from samples gathered using I22’s GiSAXS sample scanning methodology in a two-pronged approach. The first approach is to utilise classical data analysis algorithms to analyse GiSAXS data in an automated fashion, utilising Diamond’s data acquisition and analysis framework, providing beamline users with data analysis during their time at Diamond. 

The second approach, in collaboration with groups on the RAL campus (UK) and Berkeley campus (USA), would be to utilise Machine Learning algorithms in order to provide sample classifications in a timelier manner providing a pathway towards automatic, analytically driven, GiSAXS measurements and experiments. Furthermore, such classifications also have the potential to then be fed into regression driven analytical routines thus providing results for publication.