Abstract
ABSTRACT We present C luSTAR-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 H alo-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 C luSTAR-ND produce a similarly robust clustering structure, it does so in a run-time that is at least 3 orders of magnitude faster. In optimizing C luSTAR-ND’s clustering performance, we have also carefully calibrated 4 of the 7 C luSTAR-ND parameters which – unless specified by the user – will be automatically and optimally chosen based on the input data. We conclude that C luSTAR-ND is a robust astrophysical clustering algorithm that can be leveraged to find stellar satellite groups on large synthetic or observational data sets.
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