We use SDSS data to investigate the scaling relations of 127 NoSOCS and 56 CIRS galaxy clusters at low redshift ($z le 0.10$). We show that richness and both optical and X-ray luminosities are reliable mass proxies. The scatter in mass at fixed obser
vable is $sim$ 40%, depending on the aperture, sample and observable considered. For example, for the massive CIRS systems $sigma_{lnM500|N500}$ = 0.33 $pm$ 0.05 and $sigma_{lnM500|Lx}$ = 0.48 $pm$ 0.06. For the full sample $sigma_{lnM500|N500}$ = 0.43 $pm$ 0.03 and $sigma_{lnM500|Lx}$ = 0.56 $pm$ 0.06. We estimate substructure using two and three dimensional optical data, verifying that substructure has no significant effect on the cluster scaling relations (intercepts and slopes), independent of which substructure test we use. For a subset of twenty-one clusters, we estimate masses from the M-T$_X$ relation using temperature measures from BAX. The scaling relations derived from the optical and X-ray masses are indeed very similar, indicating that our method consistently estimates the cluster mass and yields equivalent results regardless of the wavelength from which we measure mass. For massive systems, we represent the mass-richness relation by a function with the form ${rm ln (M_{200}) = A + B times ln(N_{200}/60)}$, with M$_{200}$ being expressed in units of 10$^{14}$ M$_{odot}$. Using the virial mass, for CIRS clusters, we find A = (1.39 $pm$ 0.07) and B = (1.00 $pm$ 0.11). The relations based on the virial mass have a scatter of $sigma_{lnM200|N200}$ = 0.37 $pm$ 0.05, while $sigma_{lnM200|N200}$ = 0.77 $pm$ 0.22 for the caustic mass and $sigma_{lnM200|N200}$ = 0.34 $pm$ 0.08 for the temperature based mass (abridged).
We use Sloan Digital Sky Survey (SDSS) data to investigate galaxy cluster properties of systems first detected within DPOSS. With the high quality photometry of SDSS we derived new photometric redshifts and estimated richness and optical luminosity.
For a subset of low redshift ($z le 0.1$) clusters, we have used SDSS spectroscopic data to identify groups in redshift space in the region of each cluster, complemented with massive systems from the literature to assure the continuous mass sampling. A method to remove interlopers is applied, and a virial analysis is performed resulting in estimates of velocity dispersion, mass, and a physical radius for each low-$z$ system. We discuss the choice of maximum radius and luminosity range in the dynamical analysis, showing that a spectroscopic survey must be complete to at least M$^*+1$ if one wishes to obtain accurate and unbiased estimates of velocity dispersion and mass. We have measured X-ray luminosity for all clusters using archival data from RASS. For a smaller subset (twenty-one clusters) we selected temperature measures from the literature and estimated mass from the M-T$_X$ relation, finding that they show good agreement with the virial estimate. However, these two mass estimates tend to disagree with the caustic results. We measured the presence of substructure in all clusters of the sample and found that clusters with substructure have virial masses higher than those derived from T$_X$. This trend is not seen when comparing the caustic and X-ray masses. That happens because the caustic mass is estimated directly from the mass profile, so it is less affected by substructure.
We describe 2DPHOT, a general purpose analysis environment for source detection and analysis in deep wide-field images. 2DPHOT is an automated tool to obtain both integrated and surface photometry of galaxies in an image, to perform reliable star-gal
axy separation with accurate estimates of contamination at faint flux levels, and to estimate completeness of the image catalog. We describe the analysis strategy on which 2DPHOT is based, and provide a detailed description of the different algorithms implemented in the package. This new environment is intended as a dedicated tool to process the wealth of data from wide-field imaging surveys. To this end, the package is complemented by 2DGUI, an environment that allows multiple processing of data using a range of computing architectures.
