MulGuisin, a Topological Network Finder and its Performance on Galaxy Clustering

TL;DR

This paper presents MulGuisin, a novel topological clustering algorithm adapted from particle physics, which effectively identifies galaxy over-densities and networks, outperforming existing methods in simulations and observational data.

Contribution

The paper introduces MulGuisin (MGS), a new clustering algorithm that leverages topological information for galaxy clustering, demonstrating superior performance over traditional methods.

Findings

MGS efficiently identifies galaxy networks in simulated and real data.

MGS's clustering results closely resemble human visual perception.

The algorithm outperforms existing clustering methods in various datasets.

Abstract

We introduce a new clustering algorithm, MulGuisin (MGS), that can identify distinct galaxy over-densities 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 network-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 controlled data and some realistic simulation data as well as the SDSS observation data, and we demonstrate that our new algorithm finds networks most efficiently and defines galaxy networks in a way that most closely resembles human vision.