Finding galaxy clusters using Voronoi tessellations

By M. Ramella, W. Boschin, D. Fadda, M. Nonino. 2001

152 citations recorded by the source corpus.

We present an objective and automated procedure for detecting clusters of galaxies in imaging galaxy surveys. Our Voronoi Galaxy Cluster Finder (VGCF) uses galaxy positions and magnitudes to find clusters and determine their main features: size, richness and contrast above the background. The VGCF uses the Voronoi tessellation to evaluate the local density and to identify clusters as significative density fluctuations above the background. The significance threshold needs to be set by the user, but experimenting with different choices is very easy since it does not require a whole new run of the algorithm. The VGCF is non-parametric and does not smooth the data. As a consequence, clusters are identified irrespective of their shape and their identification is only slightly affected by border effects and by holes in the galaxy distribution on the sky. The algorithm is fast, and automatically assigns members to structures. A test run of the VGCF on the PDCS field centered at $\alpha = 13^{\rm h}26^{\rm m}$ and δ = +$29\degr$52´(J2000) produces 37 clusters. Of these clusters, 12 are VGCF counterparts of the 13 PDCS clusters detected at the 3 σ level and with estimated redshifts from $z=0.2$ to $z=0.6$. Of the remaining 25 systems, 2 are PDCS clusters with confidence level $< 3\sigma$ and redshift $z \leq 0.6$. Inspection of the 23 new VGCF clusters indicates that several of these clusters may have been missed by the matched filter algorithm for one or more of the following reasons: a) they are very poor, b) they are extremely elongated, c) they lie too close to a rich and/or low redshift cluster.

Subfields: Astronomy and Astrophysics, Computer Vision and Pattern Recognition, Ecology

View Paper: https://www.semanticscholar.org/paper/924209688ff154681782b147789bc56f497c12aa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Astronomy and Astrophysics

In Computer Vision and Pattern Recognition

In Ecology

Citations in the corpus

zobov: a parameter-free void-finding algorithm (2007, methodology): “There are other methods that use tessellation techniques to find clusters (Ramella et al.2001; Barkhouse et al. 2006; Söchting et al. 2006; van Breukelen et al. 2006; Melnyk, Elyiv & Vavilova 2006) or voids (Gaite 2005; Aragón-Calvo et al. 2007).”
On the efficiency and reliability of cluster mass estimates based on member galaxies (2006, methodology): “…clusters (Dalton et al. 1992; Lumsden et al. 1992; Lopes et al. 2004), and much more sophisticated algorithms than Abell’s (1958) have been applied for extracting the 2-dimensional signal produced by a galaxy overdensity (e.g. Ramella et al. 2001; Gladders & Yee 2005, and references therein).”
Galaxy halo masses and satellite fractions from galaxy–galaxy lensing in the Sloan Digital Sky Survey: stellar mass, luminosity, morphology and environment dependencies (2005, methodology): “…nth nearest neighbor (3-d or in projection), counts in an aperture (again, 3-d or in projection), and Voronoi volumes, have been used in the literature (for example, Ramella et al. 2001; Marinoni et al. 2002; Hogg et al. 2003; Balogh et al. 2004b; Blanton et al. 2005b; Cooper et al. 2005).”
CARS: the CFHTLS-Archive-Research Survey. I. Five-band multi-colour data from 37 sq. deg. CFHTLS-wid (2008, methodology): “…step in this effort, our multi-colour data are currently being used on several cluster detection algorithms: The Voronoi tessellation technique from Ramella et al. (2001), the Postman matched filter algorithm (see Postman et al. 1996) and the RedCluster Sequence technique (see Gladders & Yee 2000).”