Optical variability has proven to be an effective way of detecting AGNs in imaging surveys, lasting from weeks to years. In the present work we test its use as a tool to identify AGNs in the VST multi-epoch survey of the COSMOS field, originally tail
ored to detect supernova events. We make use of the multi-wavelength data provided by other COSMOS surveys to discuss the reliability of the method and the nature of our AGN candidates. Our selection returns a sample of 83 AGN candidates; based on a number of diagnostics, we conclude that 67 of them are confirmed AGNs (81% purity), 12 are classified as supernovae, while the nature of the remaining 4 is unknown. For the subsample of AGNs with some spectroscopic classification, we find that Type 1 are prevalent (89%) compared to Type 2 AGNs (11%). Overall, our approach is able to retrieve on average 15% of all AGNs in the field identified by means of spectroscopic or X-ray classification, with a strong dependence on the source apparent magnitude. In particular, the completeness for Type 1 AGNs is 25%, while it drops to 6% for Type 2 AGNs. The rest of the X-ray selected AGN population presents on average a larger r.m.s. variability than the bulk of non variable sources, indicating that variability detection for at least some of these objects is prevented only by the photometric accuracy of the data. We show how a longer observing baseline would return a larger sample of AGN candidates. Our results allow us to assess the usefulness of this AGN selection technique in view of future wide-field surveys.
We present an X-ray follow-up, based on XMM plus Chandra, of six Fossil Group (FG) candidates identified in our previous work using SDSS and RASS data. Four candidates (out of six) exhibit extended X-ray emission, confirming them as true FGs. For the
other two groups, the RASS emission has its origin as either an optically dull/X-ray bright AGN, or the blending of distinct X-ray sources. Using SDSS-DR7 data, we confirm, for all groups, the presence of an r-band magnitude gap between the seed elliptical and the second-rank galaxy. However, the gap value depends, up to 0.5mag, on how one estimates the seed galaxy total flux, which is greatly underestimated when using SDSS (relative to Sersic) magnitudes. This implies that many FGs may be actually missed when using SDSS data, a fact that should be carefully taken into account when comparing the observed number densities of FGs to the expectations from cosmological simulations. The similarity in the properties of seed--FG and non-fossil ellipticals, found in our previous study, extends to the sample of X-ray confirmed FGs, indicating that bright ellipticals in FGs do not represent a distinct population of galaxies. For one system, we also find that the velocity distribution of faint galaxies is bimodal, possibly showing that the system formed through the merging of two groups. This undermines the idea that all selected FGs form a population of true fossils.
We present an application of self-adaptive supervised learning classifiers derived from the Machine Learning paradigm, to the identification of candidate Globular Clusters in deep, wide-field, single band HST images. Several methods provided by the D
AME (Data Mining & Exploration) web application, were tested and compared on the NGC1399 HST data described in Paolillo 2011. The best results were obtained using a Multi Layer Perceptron with Quasi Newton learning rule which achieved a classification accuracy of 98.3%, with a completeness of 97.8% and 1.6% of contamination. An extensive set of experiments revealed that the use of accurate structural parameters (effective radius, central surface brightness) does improve the final result, but only by 5%. It is also shown that the method is capable to retrieve also extreme sources (for instance, very extended objects) which are missed by more traditional approaches.
We present the analysis of the luminosity function of a large sample of galaxy clusters from the Northern Sky Optical Cluster Survey, using latest data from the Sloan Digital Sky Survey. Our global luminosity function (down to M_r<= -16) does not sho
w the presence of an upturn at faint magnitudes, while we do observe a strong dependence of its shape on both richness and cluster-centric radius, with a brightening of M^* and an increase of the dwarf to giant ratio with richness, indicating that more massive systems are more efficient in creating/retaining a population of dwarf satellites. This is observed both within physical (0.5 R_200) and fixed (0.5 Mpc) apertures, suggesting that the trend is either due to a global effect, operating at all scales, or to a local one but operating on even smaller scales. We further observe a decrease of the relative number of dwarf galaxies towards the cluster center; this is most probably due to tidal collisions or collisional disruption of the dwarfs since merging processes are inhibited by the high velocity dispersions in cluster cores and, furthermore, we do not observe a strong dependence of the bright end on the environment. We find indication that the dwarf to giant ratio decreases with increasing redshift, within 0.07<z<0.2. We also measure a trend for stronger suppression of faint galaxies (below M^*+2) with increasing redshift in poor systems, with respect to more massive ones, indicating that the evolutionary stage of less massive galaxies depends more critically on the environment. Finally we point out that the luminosity function is far from universal; hence the uncertainties introduced by the different methods used to build a composite function may partially explain the variety of faint-end slopes reported in the literature as well as, in some cases, the presence of a faint-end upturn.
We present a wide field study of the Globular Clusters/Low Mass X-ray Binaries connection in the cD elliptical NGC1399, combining HST/ACS and Chandra high resolution data. We find evidence that LMXB formation likelihood is influenced by GCs structura
l parameters, in addition to the well known effects of mass and metallicity, independently from galactocentric distance.
