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Detecting Galaxy Clusters in the DLS and CARS: a Bayesian Cluster Finder
TL;DR: In this paper, a matched filter algorithm was proposed 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.
Abstract: The detection of galaxy clusters in present and future surveys enables measuring mass-to-light ratios, clustering properties or 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 We tested the algorithm through realistic mock galaxy catalogs, revealing that the detections are 100% complete and 80% pure for clusters up to z 25 (Abell Richness > 0) We applied the algorithm to the CFHTLS Archive Research Survey (CARS) data, recovering similar detections as previously published using the same data plus additional clusters that are very probably real We also applied this algorithm to the Deep Lens Survey (DLS), obtaining the first sample of optical-selected galaxy in this survey The sample is complete up to redshift 07 and we detect more than 780 cluster candidates up to redshift 12 We conclude by discussing the differences between previous weak lensing detections in this survey and optical detections in both samples
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TL;DR: In this paper, the same method was applied to the full CFHTLS Wide Survey (CFHTLS) Deep 1 field by Mazure et al. and the results showed that 4061 candidate clusters at 3σ or above (6802 at 2σ and above), in the redshift range 0.1 ≤ z ≤ 1.15, with estimated mean masses between 1.3 × 10 14 and 12.6 × 1014 M�.
Abstract: Context. Cosmological parameters can be constrained by counting clusters of galaxies as a function of mass and redshift and by considering regions of the sky sampled as deeply and as homogeneously as possible. Aims. Several methods for detecting clusters in large imaging surveys have been developed, among which the one used here, which is based on detecting structures. This method was first applied to the Canada France Hawaii Telescope Legacy Survey (CFHTLS) Deep 1 field by Mazure et al. (2007, A&A, 467, 49), then to all the Deep and Wide CFHTLS fields available in the T0004 data release by Adami et al. (2010, A&A, 509, A81). The validity of the cluster detection rate was estimated by applying the same procedure to galaxies from the Millennium simulation. Here we use the same method to analyse the full CFHTLS Wide survey, based on the T0006 data release. Methods. Our method is based on the photometric redshifts computed with Le Phare for all the galaxies detected in the Wide fields, limited to magnitudes z � ≤ 22.5. We constructed galaxy density maps in photometric redshift bins of 0.1 based on an adaptive kernel technique, detected structures with SExtractor at various detection levels, and built cluster catalogues by applying a minimal spanning tree algorithm. Results. In a total area of 154 deg 2 , we have detected 4061 candidate clusters at 3σ or above (6802 at 2σ and above), in the redshift range 0.1 ≤ z ≤ 1.15, with estimated mean masses between 1.3 × 10 14 and 12.6 × 10 14 M� . This catalogue of candidate clusters will be available at the CDS. We compare our detections with those made in various CFHTLS analyses with other methods. By stacking a subsample of clusters, we show that this subsample has typical cluster characteristics (colour−magnitude relation, galaxy luminosity function). We also confirm that the cluster-cluster correlation function is comparable to the one obtained for other cluster surveys and analyse large-scale filamentary galaxy distributions. Conclusions. We have increased the number of known optical high-redshift cluster candidates by a large factor, an important step towards obtaining reliable cluster counts to measure cosmological parameters. The clusters that we detect behave as expected if they are located at the intersection of filaments by which they are fed.
62 citations
Cites methods from "Detecting Galaxy Clusters in the DL..."
...Ascaso et al. (2010) applied a Bayesian cluster finder to detect galaxy clusters in several surveys including the CFHTLS, and have detected 90% of the clusters found by Olsen et al. (2007) and Adami et al. (2010, hereafter A10)....
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TL;DR: In this article, a method for detecting clusters in large imaging surveys, based on the detection of structures in galaxy density maps made in slices of photometric redshifts, was developed.
Abstract: We have developed a method for detecting clusters in large imaging surveys, based on the detection of structures in galaxy density maps made in slices of photometric redshifts. This method was first applied to the Canada France Hawaii Telescope Legacy Survey (CFHTLS) Deep 1 field by Mazure et al. (2007), then to all the Deep and Wide CFHTLS fields available in the T0004 data release by Adami et al. (2010). The validity of the cluster detection rate was estimated by applying the same procedure to galaxies from the Millennium simulation. Here we analyse with the same method the full CFHTLS Wide survey, based on the T0006 data release. In a total area of 154 deg2, we have detected 4061 candidate clusters at 3sigma or above (6802 at 2sigma and above), in the redshift range 0.1<=z<=1.15, with estimated mean masses between 1.3 10^14 and 12.6 10^14 M_solar. This catalogue of candidate clusters will be available online via VizieR. We compare our detections with those made in various CFHTLS analyses with other methods. By stacking a subsample of clusters, we show that this subsample has typical cluster characteristics (colour-magnitude relation, galaxy luminosity function). We also confirm that the cluster-cluster correlation function is comparable to that obtained for other cluster surveys and analyze large scale filamentary galaxy distributions. We have increased the number of known optical high redshift cluster candidates by a large factor, an important step towards obtaining reliable cluster counts to measure cosmological parameters. The clusters that we detect behave as expected for a sample of clusters fed by filaments at the intersection of which they are located.
5 citations