Observing flow of He II with unsupervised machine learning

Time dependent observations of point-to-point correlations of the velocity vector field (structure functions) are necessary to model and understand fluid flow around complex objects. Using thermal gradients, we observed fluid flow by recording fluorescence of He-2* excimers produced by neutron capture throughout a similar to cm(3) volume. Because the photon emitted by an excited excimer is unlikely to be recorded by the camera, the techniques of particle tracking (PTV) and particle imaging (PIV) velocimetry cannot be applied to extract information from the fluorescence of individual excimers. Therefore, we applied an unsupervised machine learning algorithm to identify light from ensembles of excimers (clusters) and then tracked the centroids of the clusters using a particle displacement determination algorithm developed for PTV.

Automated summary

Time dependent observations of point-to-point correlations of the velocity vector field are important for modeling and understanding fluid flow around complex objects. This study used thermal gradients to observe fluid flow through fluorescence recording, and applied unsupervised machine learning algorithms and particle displacement determination algorithms to analyze the recorded fluorescence.