Probabilistic Analysis of Event-Mode Experimental Data

TL;DR

This paper introduces a new probabilistic analysis method for neutron and x-ray scattering event data that avoids histogramming and least-squares fitting, improving efficiency and reducing systematic errors.

Contribution

The study presents a novel analysis approach that directly models event data probabilistically, eliminating the need for histogramming and least-squares fitting.

Findings

More efficient data analysis requiring fewer data points.

Reduced systematic errors compared to traditional histogram-based methods.

Increased computational time and less intuitive analysis process.

Abstract

Neutron and x-ray scattering experiments traditionally rely upon histogrammed data sets, which are analysed using least-squares curve fitting of multiple probability distribution components to quantify separately the various scientific contributions of interest. The main advantage to these methods is the relative ease of deployment due to their intuitive nature. Despite great popularity, these methods have known drawbacks, which can cause systematic errors and biases in some common scenarios in this field. Improvements over the base methods include dynamic optimisation of histogram bin width and the application of modern numerical optimisation methods that have greater stability, but, whilst reduced, the systematic effects carried by this stack nonetheless remain. In this study, we demonstrate analysis of neutron scattering event data using neither any numerical integration or histogramming steps, nor least squares fitting. The benefits of the new methodology are a greater efficiency (i.e. fewer data points required for the same parameter accuracy) and a reduced impact of inherent systematic error. The main drawbacks are a less intuitive analysis method and an increase in computation time.