Distributed, Scalable Clustering for Detecting Halos in Terascale Astronomy Datasets

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Provided by: University of Texas at Arlington
Topic: Big Data
Format: PDF
Terascale astronomical datasets have the potential to provide unprecedented insights into the origins of universe. However, automated techniques for determining regions of interest are a must if domain experts are to cope with the intractable amounts of simulation data. This paper addresses the important problem of locating and tracking high density regions in space that generally correspond to halos and sub-halos and host galaxies. A density based, mode following clustering method called Automated Hierarchical Density Shaving (Auto-HDS) is adapted for this application. Auto-HDS can detect clusters of different densities while discarding the vast majority of background data.
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