MulGuisin, a Topological Clustering Algorithm, and Its Performance as a Cosmic Structure Finder 后印本

摘要: We introduce a new clustering algorithm, MulGuisin (MGS), that can find galaxy clusters using topological information from the galaxy distribution. This algorithm was first introduced in an LHC experiment as a Jet Finder software, which looks for particles that clump together in close proximity. The algorithm preferentially considers particles with high energies and merges them only when they are closer than a certain distance to create a jet. MGS shares some similarities with the minimum spanning tree (MST) since it provides both clustering and graph-based topology information. Also, similar to the density-based spatial clustering of applications with noise (DBSCAN), MGS uses the ranking or the local density of each particle to construct clustering. In this paper, we compare the performances of clustering algorithms using some controlled data and some realistic simulation data as well as the SDSS observation data, and we demonstrate that our new algorithm find clusters most efficiently and it defines galaxy clusters in a way that most closely resembles human vision.