Bayesian Cluster Finder: Clusters in the CFHTLS Archive Research Survey
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TLDR
In this paper, the authors presented a new technique for detecting galaxy clusters based on the Matched Filter Algorithm from a Bayesian point of view, which is able to determine the position, redshift and richness of the cluster through the maximization of a filter depending on galaxy luminosity, density and photometric redshift combined with a galaxy cluster prior that accounts for color-magnitude relations and BCG-redshift relation.Abstract:
The detection of galaxy clusters in present and future surveys enables measuring mass-to-light ratios, clustering properties, galaxy cluster abundances and therefore, constraining cosmological parameters. We present a new technique for detecting galaxy clusters, which is based on the Matched Filter Algorithm from a Bayesian point of view. The method is able to determine the position, redshift and richness of the cluster through the maximization of a filter depending on galaxy luminosity, density and photometric redshift combined with a galaxy cluster prior that accounts for color-magnitude relations and BCG-redshift relation. We tested the algorithm through realistic mock galaxy catalogs, revealing that the detections are 100% complete and 80% pure for clusters up to z $ $20 (Abell Richness $\sim$0, M$\sim4\times10^{14} M_{\odot}$). The completeness and purity remains approximately the same if we do not include the prior information, implying that this method is able to detect galaxy cluster with and without a well defined red sequence. We applied the algorithm to the CFHTLS Archive Research Survey (CARS) data, recovering similar detections as previously published using the same or deeper data plus additional clusters which appear to be real.read more
Citations
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Journal ArticleDOI
The Integrated Cluster Finder for the ARCHES project
Alexey Mints,Alexey Mints,Axel Schwope,Simon Rosen,François-Xavier Pineau,Francisco J. Carrera +5 more
TL;DR: The Integrated Cluster Finder (ICF) as discussed by the authors is a tool to find clusters by determining the overdensities of potential member galaxies in optical and infrared catalogues, based on a spectroscopic meta-catalogue.
References
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Journal ArticleDOI
A Universal Density Profile from Hierarchical Clustering
TL;DR: In this article, the authors used high-resolution N-body simulations to study the equilibrium density profiles of dark matter halos in hierarchically clustering universes, and they found that all such profiles have the same shape, independent of the halo mass, the initial density fluctuation spectrum, and the values of the cosmological parameters.
Journal ArticleDOI
An analytic expression for the luminosity function for galaxies
TL;DR: In this paper, a new analytic approximation for the luminosity function for galaxies is proposed, which shows good agreement with both a luminosity distribution for bright nearby galaxies and a composite luminosity distributions for cluster galaxies.
Journal ArticleDOI
Galaxy formation through hierarchical clustering
TL;DR: In this article, the formation of galaxies by gas condensation within massive dark halos is studied, where the structure grows through hierarchical clustering of a mixture of gas and dissipationless dark matter.
Journal ArticleDOI
Bias and variance of angular correlation functions
TL;DR: In this article, a general method for calculating the bias and variance of estimators for w(θ) based on galaxy-galaxy (DD), random-random (RR), and galaxy random (DR) pair counts is presented.
Journal ArticleDOI
K-corrections and filter transformations in the ultraviolet, optical, and near infrared
Michael R. Blanton,Sam T. Roweis +1 more
TL;DR: In this article, a nonnegative matrix factorization (NVMF) approach is proposed to construct model-based template sets given a set of heterogeneous photometric and spectroscopic galaxy data.
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