Structure detection in the D1 CFHTLS deep field using accurate photometric redshifts: a benchmark

Aims. We investigate structures in the D1 CFHTLS deep field to test the method that will be applied to generate homogeneous samples of clusters and groups of galaxies in order to constrain the cosmology and detailed physics of groups and clusters. Methods. An adaptive kernel technique was applied to 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 in efficiency with increasing background) is overcome 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 with the first concern using random distributions. For the second, comparison with detailed simulations is foreseen but we used a pragmatic approach by comparing our results to GalICS simulations to check that our detection number is not totally at odds with cosmological simulations. We used the XMM-LSS survey and secured VVDS redshifts up to z similar to 1 to check individual detections. Results. We show that our detection method is basically able to recover 100% of the C1 XMM-LSS X-ray detections ( in the regions in common) 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.