Discovering Features in Sr$_{14}$Cu$_{24}$O$_{41}$ Neutron Single Crystal Diffraction Data by Cluster Analysis

TL;DR

This paper applies a clustering algorithm to neutron diffraction data of Sr$_{14}$Cu$_{24}$O$_{41}$ to differentiate between Bragg peaks, diffuse scattering, and other features, revealing complex reciprocal space structures.

Contribution

The study introduces a clustering-based method to identify and separate multiple scattering features in neutron diffraction data, including previously uncharacterized diffuse patterns.

Findings

Diffuse scattering patterns with distinguishable geometries identified.

A third feature in low signal-to-noise regions discovered, origin unknown.

Method effectively separates features in complex diffraction datasets.

Abstract

To address the SMC'18 data challenge, "Discovering Features in Sr$_{14}$Cu$_{24}$O$_{41}$", we have used the clustering algorithm "DBSCAN" to separate the diffuse scattering features from the Bragg peaks, which takes into account both spatial and photometric information in the dataset during in the clustering process. We find that, in additional to highly localized Bragg peaks, there exists broad diffuse scattering patterns consisting of distinguishable geometries. Besides these two distinctive features, we also identify a third distinguishable feature submerged in the low signal-to-noise region in the reciprocal space, whose origin remains an open question.