Structure detection in the D1 CFHTLS deep field using accurate photometric redshifts: a benchmark
Résumé
We investigate structures in the D1 CFHTLS deep field in order to test the method that will be applied to generate homogeneous samples of clusters and groups of galaxies in order to constrain cosmology and detailed physics of groups and clusters. Adaptive kernel technique is applied on galaxy catalogues. This technique needs none of the usual a-priori assumptions (luminosity function, density profile, colour of galaxies) made with other methods. Its main drawback (decrease of efficiency with increasing background) is overcame by the use of narrow slices in photometric redshift space. There are two main concerns in structure detection. One is false detection and the second, the evaluation of the selection function in particular if one wants "complete" samples. We deal here with the first concern using random distributions. For the second, comparison with detailed simulations is foreseen but we use here a pragmatic approach with comparing our results to GalICS simulations to check that our detection number is not totally at odds compared to cosmological simulations. We use XMM-LSS survey and secured VVDS redshifts up to z~1 to check individual detections. We show that our detection method is basically capable to recover (in the regions in common) 100% of the C1 XMM-LSS X-ray detections in the correct redshift range plus several other candidates. Moreover when spectroscopic data are available, we confirm our detections, even those without X-ray data.