Galaxy structure searches by photometric redshifts in the CFHTLS*
LAM, OAMP, Pôle de l'Etoile Site Château-Gombert, 38 rue Frédéric Juliot-Curie, 13388 Marseille Cedex 13, France e-mail: firstname.lastname@example.org
2 UPMC Université Paris 06, UMR 7095, Institut d'Astrophysique de Paris, 75014 Paris, France
3 CNRS, UMR 7095, Institut d'Astrophysique de Paris, 75014 Paris, France
4 OCA, Cassiopée, Boulevard de l'Observatoire, BP 4229, 06304 Nice Cedex 4, France
5 MPE, Giessenbachstrasse, 85748 Garching, Germany
6 CRAL (UMR 5574), Université Claude Bernard Lyon 1 (UCBL), École Normale Supérieure de Lyon (ENS-L), and Centre National de la Recherche Scientifique (CNRS), France
7 Canada-France-Hawaii Telescope Corporation, Kamuela, HI-96743, USA
8 INAF – Osservatorio Astronomico di Bologna, via Ranzani 1, 40127 Bologna, Italy
9 INAF IASF – Milano, via Bassini 15, 20133 Milano, Italy
10 Department Physics & Astronomy, Northwestern University, Evanston, IL 60208-2900, USA
Accepted: 20 October 2009
Context. Counting clusters is one of the methods to constrain cosmological parameters, but has been limited up to now both by the redshift range and by the relatively small sizes of the homogeneously surveyed areas.
Aims. In order to enlarge publicly available optical cluster catalogs, in particular at high redshift, we have performed a systematic search for clusters of galaxies in the Canada France Hawaii Telescope Legacy Survey (CFHTLS).
Methods. We considered the deep 2, 3 and 4 CFHTLS Deep fields (each 1 1 deg2), as well as the wide 1, 3 and 4 CFHTLS Wide fields. We used the Le Phare photometric redshifts for the galaxies detected in these fields with magnitude limits of i'=25 and 23 for the Deep and Wide fields respectively. We then constructed galaxy density maps in photometric redshift bins of 0.1 based on an adaptive kernel technique and detected structures with SExtractor at various detection levels. In order to assess the validity of our cluster detection rates, we applied a similar procedure to galaxies in Millennium simulations. We measured the correlation function of our cluster candidates. We analyzed large scale properties and substructures, including filaments, by applying a minimal spanning tree algorithm both to our data and to the Millennium simulations.
Results. We detected 1200 candidate clusters with various masses (minimal masses between 1.0 1013 and 5.5 1013 and mean masses between 1.3 1014 and 12.6 10) in the CFHTLS Deep and Wide fields, thus notably increasing the number of known high redshift cluster candidates. We found a correlation function for these objects comparable to that obtained for high redshift cluster surveys. We also show that the CFHTLS deep survey is able to trace the large scale structure of the universe up to . Our detections are fully consistent with those made in various CFHTLS analyses with other methods. We now need accurate mass determinations of these structures to constrain cosmological parameters.
Conclusions. We have shown that a search for galaxy clusters based on density maps built from galaxy catalogs in photometric redshift bins is successful and gives results comparable to or better than those obtained with other methods. By applying this technique to the CFHTLS survey we have increased the number of known optical high redshift cluster candidates by a large factor, an important step towards using cluster counts to measure cosmological parameters.
Key words: surveys / galaxies: clusters: general / large-scale structure of Universe
Based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. This work is based in part on data products produced at TERAPIX and the Canadian Astronomy Data Centre as part of the Canada-France-Hawaii Telescope Legacy Survey, a collaborative project of NRC and CNRS.
© ESO, 2010