1 research outputs found
Discovering Features in SrCuO Neutron Single Crystal Diffraction Data by Cluster Analysis
To address the SMC'18 data challenge, "Discovering Features in
SrCuO", 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