http://arxiv.org/abs/2201.10694
We present CluSTAR-ND, a fast hierarchical galaxy/(sub)halo finder that produces Clustering Structure via Transformative Aggregation and Rejection in N-Dimensions. It is designed to improve upon Halo-OPTICS — an algorithm that automatically detects and extracts significant astrophysical clusters from the 3D spatial positions of simulation particles — by decreasing run-times, possessing the capability for metric adaptivity, and being readily applicable to data with any number of features. We directly compare these algorithms and find that not only does CluSTAR-ND produce a similarly robust clustering structure, it does so in a run-time that is at least $3$ orders of magnitude faster. In optimising CluSTAR-ND’s clustering performance, we have also effectively made CluSTAR-ND a parameterless clustering algorithm since its parameters will be chosen automatically and optimally based on the input data. We conclude that CluSTAR-ND is a robust astrophysical clustering algorithm that can be leveraged to find stellar satellite groups on large synthetic or observational data sets.
W. Oliver, P. Elahi and G. Lewis
Thu, 27 Jan 22
43/44
Comments: 19 pages, 11 figures, 1 table. Submitted to MNRAS
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