We present ACS, NICMOS, and Keck AO-assisted photometry of 20 Type Ia supernovae (SNe Ia) from the HST Cluster Supernova Survey. The SNe Ia were discovered over the redshift interval 0.623 < z < 1.415. Fourteen of these SNe Ia pass our strict selection cuts and are used in combination with the world's sample of SNe Ia to derive the best current constraints on dark energy. Ten of our new SNe Ia are beyond redshift z = 1, thereby nearly doubling the statistical weight of HST-discovered SNe Ia beyond this redshift. Our detailed analysis corrects for the recently identified correlation between SN Ia luminosity and host galaxy mass and corrects the NICMOS zeropoint at the count rates appropriate for very distant SNe Ia. Adding these supernovae improves the best combined constraint on dark energy density, ρ DE (z), at redshifts 1.0 < z < 1.6 by 18% (including systematic errors). For a flat ΛCDM universe, we find Ω Λ = 0.729 +0.014 −0.014 (68% CL including systematic errors). For a flat wCDM model, we measure a constant dark energy equation-of-state parameter w = −1.013 +0.068 −0.073 (68% CL). Curvature is constrained to ∼ 0.7% in the owCDM model and to ∼ 2% in a model in which dark energy is allowed to vary with parameters w 0 and w a . Tightening further the constraints on the time evolution of dark energy will require several improvements, including high-quality multi-passband photometry of a sample of several dozen z > 1 SNe Ia. We describe how such a sample could be efficiently obtained by targeting cluster fields with WFC3 on HST.The updated supernova Union2.1 compilation of 580 SNe is available at http://supernova.lbl.gov/Union ⋆ is less than the mass threshold. We begin by noting that.We can then integrate this probability over all true host masses less than the threshold:⋆ )P (m true ⋆ ) up to a normalization constant found by requiring the integral to be unity when integrating over all possible true masses. P (m true ⋆ ) is estimated from the observed distribution for each type of survey. The SNLS (Sullivan et al. 2010) and SDSS (Lampeitl et al. 2010) host masses were assumed to be representative of untargeted surveys, while the mass distribution in Kelly et al. (2010) was assumed typical of nearby targeted surveys. As these distributions are approximately log-normal, we use this model for P (m true ⋆) using the mean and RMS from the log of the host masses from these surveys (with the average measurement errors subtracted in quadrature), giving log 10 P (m true ⋆ ) = N (µ = 9.88, σ 2 = 0.92 2 ) for untargeted surveys and log 10 P (m true ⋆ ) = N (10.75, 0.66 2 ) for targeted surveys. When host mass measurements are available, P (m obs ⋆ |m true ⋆ ) is also modeled as a log-normal; when no measurement is available, a flat distribution is used.For a supernova from an untargeted survey with no host mass measurement (including supernovae presented in this paper which are not in a cluster), P (m trueis the integral of P (m true ⋆ ) up to the threshold mass: 0.55. Similarly, nearby supernovae from targeted surveys w...

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 a 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 z ∈ [0.08,0.55]. The redMaPPer photometric redshifts are nearly Gaussian, with a scatter σ z ≈ 0.006 at z ≈ 0.1, increasing to σ z ≈ 0.02 at z ≈ 0.5 due to increased photometric noise near the survey limit. The median value for |∆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.

We present a catalog of galaxy clusters selected using the maxBCG red-sequence method from Sloan Digital Sky Survey photometric data. This catalog includes 13,823 clusters with velocity dispersions greater than %400 km s À1 and is the largest galaxy cluster catalog assembled to date. They are selected in an approximately volume-limited way from a 0.5 Gpc 3 region covering 7500 deg 2 of sky between redshifts 0.1 and 0.3. Each cluster contains between 10 and 190 E/S0 ridgeline galaxies brighter than 0.4L Ã within a scaled radius R 200 . The tight relation between ridgeline color and redshift provides an accurate photometric redshift estimate for every cluster. Photometric redshift errors are shown by comparison to spectroscopic redshifts to be small (Á z ' 0:01), essentially independent of redshift, and well determined throughout the redshift range. Runs of maxBCG on realistic mock catalogs suggest that the sample is more than 90% pure and more than 85% complete for clusters with masses !1 ; 10 14 M . Spectroscopic measurements of cluster members are used to examine line-of-sight projection as a contaminant in the identification of brightest cluster galaxies and cluster member galaxies. Spectroscopic data are also used to demonstrate the correlation between optical richness and velocity dispersion. Comparison to the combined NORAS and REFLEX X-rayYselected cluster catalogs shows that X-rayYluminous clusters are found among the optically richer maxBCG clusters. This paper is the first in a series that will consider the properties of these clusters, their galaxy populations, and their implications for cosmology.

We use the abundance and weak-lensing mass measurements of the Sloan Digital Sky Survey maxBCG cluster catalog to simultaneously constrain cosmology and the richness-mass relation of the clusters. Assuming a flat ΛCDM cosmology, we find σ 8 (Ω m /0.25) 0.41 = 0.832 ± 0.033 after marginalization over all systematics. In common with previous studies, our error budget is dominated by systematic uncertainties, the primary two being the absolute mass scale of the weak-lensing masses of the maxBCG clusters, and uncertainty in the scatter of the richness-mass relation. Our constraints are fully consistent with the WMAP five-year data, and in a joint analysis we find σ 8 = 0.807 ± 0.020 and Ω m = 0.265 ± 0.016, an improvement of nearly a factor of 2 relative to WMAP5 alone. Our results are also in excellent agreement with and comparable in precision to the latest cosmological constraints from X-ray cluster abundances. The remarkable consistency among these results demonstrates that cluster abundance constraints are not only tight but also robust, and highlight the power of optically selected cluster samples to produce precision constraints on cosmological parameters.

Measurements of galaxy cluster abundances, clustering properties, and massto-light ratios in current and future surveys can provide important cosmological constraints. Digital wide-field imaging surveys, the recently-demonstrated fidelity of red-sequence cluster detection techniques, and a new generation of realistic mock galaxy surveys provide the means for construction of large, cosmologically-interesting cluster samples, whose selection and properties can be understood in unprecedented depth. We present the details of the "maxBCG" algorithm, a cluster-detection technique tailored to multi-band CCD-imaging data. MaxBCG primarily relies on an observational cornerstone of massive galaxy clusters: they are marked by an overdensity of bright, uniformly red galaxies. This detection scheme also exploits classical brightest cluster galaxies (BCGs), which are often found at the center of these same massive clusters. We study the algorithm herein through its performance on large, realistic, mock galaxy catalogs, -2which reveal that it is > 90% pure for clusters at 0.1 < z < 0.3 with 10 or more red galaxies, and > 90% complete for halos at 0.1 < z < 0.3 with masses of > 2 × 10 14 h −1 M ⊙ . MaxBCG is able to approximately recover the underlying halo abundance function, and assign cluster richnesses strongly coupled to the underlying halo properties. The same tests indicate that maxBCG rarely fragments halos, occasionally overmerges line-of-sight neighboring (≃ 10h −1 Mpc) halos, and overestimates the intrinsic halo red sequence galaxy population by no more than 20%. This study concludes with a discussion of considerations for cosmological measurements with such catalogs, including modeling the selection function, the role of photometric errors, the possible cosmological dependence of richness measurements, and fair cluster selection across broad redshift ranges employing multiple bandpasses.

We study the mass distribution of a sample of 28 galaxy clusters using strong and weak lensing observations. The clusters are selected via their strong lensing properties as part of the Sloan Giant Arcs Survey (SGAS) from the Sloan Digital Sky Survey (SDSS). Mass modelling of the strong lensing information from the giant arcs is combined with weak lensing measurements from deep Subaru/Suprime-cam images to primarily obtain robust constraints on the concentration parameter and the shape of the mass distribution. We find that the concentration c vir is a steep function of the mass, c vir ∝ M −0.59±0.12 vir , with the value roughly consistent with the lensing-bias-corrected theoretical expectation for high-mass (∼10 15 h −1 M ) clusters. However, the observationally inferred concentration parameters appear to be much higher at lower masses (∼10 14 h −1 M ), possibly a consequence of the modification to the inner density profiles provided by baryon cooling. The steep mass-concentration relation is also supported from direct stacking analysis of the tangential shear profiles. In addition, we explore the 2D shape of the projected mass distribution by stacking weak lensing shear maps of individual clusters with prior information on the position angle from strong lens modelling, and find significant evidence for a large mean ellipticity with the best-fitting value of e = 0.47 ± 0.06 for the mass distribution of the stacked sample. We find that the luminous cluster member galaxy distribution traces the overall mass distribution very well, although the distribution of fainter cluster galaxies appears to be more extended than the total mass.

We present a large catalog of optically selected galaxy clusters from the application of a new Gaussian Mixture Brightest Cluster Galaxy (GMBCG) algorithm to SDSS Data Release 7 data. The algorithm detects clusters by identifying the red sequence plus Brightest Cluster Galaxy (BCG) feature, which is unique for galaxy clusters and does not exist among field galaxies. Red sequence clustering in color space is detected using an Error Corrected Gaussian Mixture Model. We run GMBCG on 8240 square degrees of photometric data from SDSS DR7 to assemble the largest ever optical galaxy cluster catalog, consisting of over 55,000 rich clusters across the redshift range from 0.1 < z < 0.55. We present Monte Carlo tests of completeness and purity and perform cross-matching with X-ray clusters and with the maxBCG sample at low redshift. These tests indicate high completeness and purity across the full redshift range for clusters with 15 or more members. The catalog can be accessed from the following website:a Although the 4000Åbreak starts shifting into SDSS r band at z ∼ 0.36, we observed that g − r color is still better than r − i color for detecting red sequence up to redshift 0.43.

Imaging data from the Sloan Digital Sky Survey are used to characterize the population of galaxies in groups and clusters detected with the MaxBCG algorithm. We investigate the dependence of Brightest Cluster Galaxy (BCG) luminosity, and the distributions of satellite galaxy luminosity and satellite color, on cluster properties over the redshift range 0.1 ≤ z ≤ 0.3. The size of the dataset allows us to make measurements in many bins of cluster richness, radius and redshift. We find that, within r 200 of clusters with mass above 3×10 13 h −1 M ⊙ , the luminosity function of both red and blue satellites is only weakly dependent on richness. We further find that the shape of the satellite luminosity function does not depend on cluster-centric distance for magnitudes brighter than 0.25 M i -5log 10 h = −19. However, the mix of faint red and blue galaxies changes dramatically. The satellite red fraction is dependent on cluster-centric distance, galaxy luminosity and cluster mass, and also increases by ∼5% between redshifts 0.28 and 0.2, independent of richness. We find that BCG luminosity is tightly correlated with cluster richness, scaling as L BCG ∼ M 0.3 200 , and has a Gaussian distribution at fixed richness, with σ logL ∼ 0.17 for massive clusters. The ratios of BCG luminosity to total cluster luminosity and characteristic satellite luminosity scale strongly with cluster richness: in richer systems, BCGs contribute a smaller fraction of the total light, but are brighter compared to typical satellites. This study demonstrates the power of cross-correlation techniques for measuring galaxy populations in purely photometric data.