We present a catalogue of 348 galaxy clusters and groups with $0.2<z<1.2$ selected in the 2.78 $deg^2$ ALHAMBRA Survey. The high precision of our photometric redshifts, close to $1%$, and the wide spread of the seven ALHAMBRA pointings ensure that th
is catalogue has better mass sensitivity and is less affected by cosmic variance than comparable samples. The detection has been carried out with the Bayesian Cluster Finder (BCF), whose performance has been checked in ALHAMBRA-like light-cone mock catalogues. Great care has been taken to ensure that the observable properties of the mocks photometry accurately correspond to those of real catalogues. From our simulations, we expect to detect galaxy clusters and groups with both $70%$ completeness and purity down to dark matter halo masses of $M_hsim3times10^{13}rm M_{odot}$ for $z<0.85$. Cluster redshifts are expected to be recovered with $sim0.6%$ precision for $z<1$. We also expect to measure cluster masses with $sigma_{M_h|M^*_{CL}}sim0.25-0.35, dex$ precision down to $sim3times10^{13}rm M_{odot}$, masses which are $50%$ smaller than those reached by similar work. We have compared these detections with previous optical, spectroscopic and X-rays work, finding an excellent agreement with the rates reported from the simulations. We have also explored the overall properties of these detections such as the presence of a colour-magnitude relation, the evolution of the photometric blue fraction and the clustering of these sources in the different ALHAMBRA fields. Despite the small numbers, we observe tentative evidence that, for a fixed stellar mass, the environment is playing a crucial role at lower redshifts (z$<$0.5).
Measurements of clustering in large-scale imaging surveys that make use of photometric redshifts depend on the uncertainties in the redshift determination. We have used light-cone simulations to show how the deprojection method successfully recovers
the real space correlation function when applied to mock photometric redshift surveys. We study how the errors in the redshift determination affect the quality of the recovered two-point correlation function. Considering the expected errors associated to the planned photometric redshift surveys, we conclude that this method provides information on the clustering of matter useful for the estimation of cosmological parameters that depend on the large scale distribution of galaxies.
A study of the galaxy distribution in the field of the elliptical galaxy NGC1600 has been undertaken. Although this galaxy is often classified as a member of a loose group, all the neighbouring galaxies are much fainter and could be taken as satellit
es of NGC1600. The number density profile of galaxies in the field of this galaxy shows a decline with radius, with evidence of a background at approximately 1.3 Mpc. The density and number density profile are consistent with that found for other isolated early-type galaxies. NGC1600 appears as an extended source in X-rays, and the center of the X-ray emission seems not to coincide with the center of the galaxy. The velocity distribution of neighbouring galaxies has been measured from optical spectroscopic observations and shows that the mean radial velocity is approximately 85 km/s less than that of NGC1600, indicating that the centre of mass could lie outside the galaxy. The velocity dispersion of the `group is estimated at 429 km/s. The inferred mass of the system is therefore of the order of 10^14 solar masses, a value that corresponds to a large group. NGC1600 therefore shares some similarities, but is not identical to, the `fossil clusters detected in X-ray surveys. Implications of this result for studies of isolated early-type galaxies are briefly discussed.
Community definitions usually focus on edges, inside and between the communities. However, the high density of edges within a community determines correlations between nodes going beyond nearest-neighbours, and which are indicated by the presence of
motifs. We show how motifs can be used to define general classes of nodes, including communities, by extending the mathematical expression of Newman-Girvan modularity. We construct then a general framework and apply it to some synthetic and real networks.
