Volumetric Data Exploration with Machine Learning-Aided Visualization in Neutron Science
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Recent advancements in neutron and x-ray sources, instrumentation and data collection modes have significantly increased the experimental data size (which could easily contain 10^8-10^10 points), so that conventional volumetric visualization approaches become inefficient for both still imaging and interactive OpenGL rendition in a 3-D setting. We introduce a new approach based on the unsupervised machine learning algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to efficiently analyze and visualize large volumetric datasets. Here we present two examples, including a single crystal diffuse scattering dataset and a neutron tomography dataset. We found that by using the intensity as the weight factor during clustering, the algorithm becomes very effective in de-noising and feature/boundary detection, and thus enables better visualization of the hierarchical internal structures of the scattering data.
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