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 correctly and defines galaxy networks in a way that most closely resembles human vision.

The FastSound project is a galaxy redshift survey using Subaru/FMOS to detect Hα emitting galaxies at z ~ 1.3, for the purpose of probing the origin of the accelerated expansion of the universe. The survey has detected ~4,000 galaxy redshifts in a total area of 30 deg2, and detected the redshift space distortion at this redshift range for the first time. The redshift space distortion (RSD) signal will be used to derive a measurement of the growth rate of large scale structure, which will provide a test for modified gravity as a possible origin of accelerated cosmic explansion. Here we present an overview and the current status of the project.