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Register a new userCalibrating the fundamental plane with SDSS DR8 data
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We present a calibration of the fundamental plane using SDSS Data Release 8. We analysed about 93000 elliptical galaxies up to $z<0.2$, the largest sample used for the calibration of the fundamental plane so far. We incorporated up-to-date K-corrections and used GalaxyZoo data to classify the galaxies in our sample. We derived independent fundamental plane fits in all five Sloan filters u, g, r, i and z. A direct fit using a volume-weighted least-squares method was applied to obtain the coefficients of the fundamental plane, which implicitly corrects for the Malmquist bias. We achieved an accuracy of 15% for the fundamental plane as a distance indicator. We provide a detailed discussion on the calibrations and their influence on the resulting fits. These re-calibrated fundamental plane relations form a well-suited anchor for large-scale peculiar-velocity studies in the nearby universe. In addition to the fundamental plane, we discuss the redshift distribution of the elliptical galaxies and their global parameters.
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We describe redMaPPer, a new red-sequence cluster finder specifically designed to make optimal use of ongoing and near-future large photometric surveys. The algorithm has multiple attractive features: (1) It can iteratively self-train the red-sequence model based on minimal spectroscopic training sample, an important feature for high redshift surveys; (2) It can handle complex masks with varying depth; (3) It produces cluster-appropriate random points to enable large-scale structure studies; (4) All clusters are assigned a full redshift probability distribution P(z); (5) Similarly, clusters can have multiple candidate central galaxies, each with corresponding centering probabilities; (6) The algorithm is parallel and numerically efficient: it can run a Dark Energy Survey-like catalog in ~500 CPU hours; (7) The algorithm exhibits excellent photometric redshift performance, the richness estimates are tightly correlated with external mass proxies, and the completeness and purity of the corresponding catalogs is superb. We apply the redMaPPer algorithm to ~10,000 deg^2 of SDSS DR8 data, and present the resulting catalog of ~25,000 clusters over the redshift range 0.08<z<0.55. The redMaPPer photometric redshifts are nearly Gaussian, with a scatter sigma_z ~ 0.006 at z~0.1, increasing to sigma_z~0.02 at z~0.5 due to increased photometric noise near the survey limit. The median value for |Delta z|/(1+z) for the full sample is 0.006. The incidence of projection effects is low (<=5%). Detailed performance comparisons of the redMaPPer DR8 cluster catalog to X-ray and SZ catalogs are presented in a companion paper (Rozo & Rykoff 2014).
We present redshift probability distributions for galaxies in the SDSS DR8 imaging data. We used the nearest-neighbor weighting algorithm presented in Lima et al. 2008 and Cunha et al. 2009 to derive the ensemble redshift distribution N(z), and individual redshift probability distributions P(z) for galaxies with r < 21.8. As part of this technique, we calculated weights for a set of training galaxies with known redshifts such that their density distribution in five dimensional color-magnitude space was proportional to that of the photometry-only sample, producing a nearly fair sample in that space. We then estimated the ensemble N(z) of the photometric sample by constructing a weighted histogram of the training set redshifts. We derived P(z) s for individual objects using the same technique, but limiting to training set objects from the local color-magnitude space around each photometric object. Using the P(z) for each galaxy, rather than an ensemble N(z), can reduce the statistical error in measurements that depend on the redshifts of individual galaxies. The spectroscopic training sample is substantially larger than that used for the DR7 release, and the newly added PRIMUS catalog is now the most important training set used in this analysis by a wide margin. We expect the primary source of error in the N(z) reconstruction is sample variance: the training sets are drawn from relatively small volumes of space. Using simulations we estimated the uncertainty in N(z) at a given redshift is 10-15%. The uncertainty on calculations incorporating N(z) or P(z) depends on how they are used; we discuss the case of weak lensing measurements. The P(z) catalog is publicly available from the SDSS website.
We study the relations between the multimodality of galaxy clusters drawn from the SDSS DR8 and the environment where they reside. As cluster environment we consider the global luminosity density field, supercluster membership, and supercluster morphology. We use 3D normal mixture modelling, the Dressler-Shectman test, and the peculiar velocity of cluster main galaxies as signatures of multimodality of clusters. We calculate the luminosity density field to study the environmental densities around clusters, and to find superclusters where clusters reside. We determine the morphology of superclusters with the Minkowski functionals and compare the properties of clusters in superclusters of different morphology. We apply principal component analysis to study the relations between the multimodality parametres of clusters and their environment simultaneously. We find that multimodal clusters reside in higher density environment than unimodal clusters. Clusters in superclusters have higher probability to have substructure than isolated clusters. The superclusters can be divided into two main morphological types, spiders and filaments. Clusters in superclusters of spider morphology have higher probabilities to have substructure and larger peculiar velocities of their main galaxies than clusters in superclusters of filament morphology. The most luminous clusters are located in the high-density cores of rich superclusters. Five of seven most luminous clusters, and five of seven most multimodal clusters reside in spider-type superclusters; four of seven most unimodal clusters reside in filament-type superclusters. Our study shows the importance of the role of superclusters as high density environment which affects the properties of galaxy systems in them.
In order to place constraints on cosmology through optical surveys of galaxy clusters, one must first understand the properties of those clusters. To this end, we introduce the Mass Analysis Tool for Chandra (MATCha), a pipeline which uses a parallellized algorithm to analyze archival Chandra data. MATCha simultaneously calculates X-ray temperatures and luminosities and performs centering measurements for hundreds of potential galaxy clusters using archival X-ray exposures. We run MATCha on the redMaPPer SDSS DR8 cluster catalog and use MATChas output X-ray temperatures and luminosities to analyze the galaxy cluster temperature-richness, luminosity-richness, luminosity-temperature, and temperature-luminosity scaling relations. We detect 447 clusters and determine 246 r2500 temperatures across all redshifts. Within 0.1 < z < 0.35 we find that r2500 Tx scales with optical richness as ln(kB Tx / 1.0 keV) = (0.52 pm 0.05) ln({lambda}/70) + (1.85 pm 0.03) with intrinsic scatter of 0.27 pm 0.02 (1 {sigma}). We investigate the distribution of offsets between the X-ray center and redMaPPer center within 0.1 < z < 0.35, finding that 68.3 pm 6.5% of clusters are well-centered. However, we find a broad tail of large offsets in this distribution, and we explore some of the causes of redMaPPer miscentering.
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