We evaluate the performance of the Sloan Digital Sky Survey (SDSS) DR8 redMaPPer photometric cluster catalog by comparing it to overlapping X-ray- and Sunyaev-Zeldovich (SZ)-selected catalogs from ...the literature. We confirm that the redMaPPer photometric redshifts are nearly unbiased (left angle bracket Delta zright angle bracket) < or =, slant 0.005), have low scatter (sigmaz approximately 0.006-0.02, depending on redshift), and have a low catastrophic failure rate ( approximately 1%). Both the T sub(X)-lambda and M sub(gas)-lambda scaling relations are consistent with a mass scatter of sigma sub(ln )M|lambda approximately 25%, albeit with a approximately 1% outlier rate due to projection effects (lambda is the cluster richness estimated employed by redMaPPer). This failure rate is somewhat lower than that expected for the full cluster sample but is consistent with the additional selection effects introduced by our reliance on X-ray and SZ selected reference cluster samples. Where the redMaPPer DR8 catalog is volume-limited (z < or =, slant 0.35), the catalog is 100% complete above T sub(X) gap 3.5 keV, and L sub(X) gap 2 x 10 super(44) erg s super(-1), decreasing to 90% completeness at L sub(X) approximately 10 super(43) erg s super(-1). All rich (lambda gap 100), low-redshift (z lap 0.25) redMaPPer clusters are X-ray-detected in the ROSAT All Sky Survey, and 86% of the clusters are correctly centered. Compared to other SDSS photometric cluster catalogs, redMaPPer has the highest completeness and purity, and the best photometric redshift performance, though some algorithms do achieve comparable performance to redMaPPer in subsets of the above categories and/or in limited redshift ranges. The redMaPPer richness is clearly the one that best correlates with X-ray temperature and gas mass. Most algorithms (including redMaPPer) have very similar centering performance as tested by comparing against X-ray centers, with only one exception which performs worse.
La investigación se basó en la revisión de diagnósticos reportados, posteriormente se ejecutó la evaluación clínica de los pacientes y basados en los hallazgos clínicos, se corroboró el reportado y ...se generó un diagnóstico asociado a la semiología encontrada para cada niño que se encuentra matriculada en el Instituto técnico Guaimaral Sede Alma Luz Vega de Cúcuta. Finalmente se analizó el porcentaje del presunto error diagnóstico encontrado. Objetivo: Caracterizar los diagnósticos reportados en las historias personales y su concordancia con el diagnóstico presuntivo emitido, según las características semiológicas de la evaluación por Fisioterapia y Neurorehabilitación. Resultados: Se identificó en el 100% de los casos se encuentran dos o más diagnósticos por sujeto; reportándose 90 diagnósticos diferentes, que representa una proporción de 0.82 diagnósticos en la población, refiriendo en algunos casos las manifestaciones clínicas de una patología han sido establecidas como un diagnóstico. Discusión: El error y/o omisión diagnóstica encontrado, indica que es un problema que se ha venido presentando, que no se ha logrado corregir o por lo menos no se había identificado, haciendo que los tratamientos no sean enfocados de manera objetiva evitando una evolución favorable.
In order to study the galaxy population of galaxy clusters with photometric data, one must be able to accurately discriminate between cluster members and non-members. The redMaPPer cluster finding ...algorithm treats this problem probabilistically, focusing exclusively on the red galaxy population. Here, we utilize Sloan Digital Sky Survey (SDSS) and Galaxy And Mass Assembly spectroscopic membership rates to validate the redMaPPer membership probability estimates for clusters with z ∈ 0.1, 0.3. We find small – but correctable – biases, sourced by three different systematics. The first two were expected a priori, namely blue cluster galaxies and correlated structure along the line of sight. The third systematic is new: the redMaPPer template fitting exhibits a non-trivial dependence on photometric noise, which biases the original redMaPPer probabilities when utilizing noisy data. After correcting for these effects, we find exquisite agreement (≈1 per cent) between the photometric probability estimates and the spectroscopic membership rates, demonstrating that we can robustly recover cluster membership estimates from photometric data alone. As a byproduct of our analysis we find that on average unavoidable projection effects from correlated structure contribute ≈6 per cent of the richness of a redMaPPer galaxy cluster. This work also marks the second public release of the SDSS redMaPPer cluster catalogue.
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 are superb. We apply the redMaPPer algorithm to ~10,000 deg super(2) of SDSS DR8 data and present the resulting catalog of ~25,000 clusters over the redshift range z isin 0.08, 0.55. The redMaPPer photometric redshifts are nearly Gaussian, with a scatter sigma sub(z) approximately 0.006 at z approximately 0.1, increasing to sigma sub(z) approximately 0.02 at z approximately 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 (< or =, slant5%). Detailed performance comparisons of the redMaPPer DR8 cluster catalog to X-ray and Sunyaev-Zel'dovich catalogs are presented in a companion paper.
We demonstrate that optical data from Sloan Digital Sky Survey, X-ray data from ROSAT and Chandra, and Sunyaev-Zel'dovich (SZ) data from Planck can be modelled in a fully self-consistent manner. ...After accounting for systematic errors and allowing for property covariance, we find that scaling relations derived from optical and X-ray selected cluster samples are consistent with one another. Moreover, these cluster scaling relations satisfy several non-trivial spatial abundance constraints and closure relations. Given the good agreement between optical and X-ray samples, we combine the two and derive a joint set of L
X-M and Y
SZ-M relations. Our best-fitting Y
SZ-M relation is in good agreement with the observed amplitude of the thermal SZ power spectrum for a Wilkinson Microwave Anisotropy Probe 7 cosmology, and is consistent with the masses for the two CLASH galaxy clusters published thus far. We predict the halo masses of the remaining z ≤ 0.4 CLASH clusters, and use our scaling relations to compare our results with a variety of X-ray and weak lensing cluster masses from the literature.
SPIDERS (The SPectroscopic IDentification of eROSITA Sources) is a programme dedicated to the homogeneous and complete spectroscopic follow-up of X-ray active galactic nuclei and galaxy clusters over ...a large area (~7500 deg super( 2)) of the extragalactic sky. SPIDERS is part of the Sloan Digital Sky Survey (SDSS)-IV project, together with the Extended Baryon Oscillation Spectroscopic Survey and the Time-Domain Spectroscopic Survey. This paper describes the largest project within SPIDERS before the launch of eROSITA: an optical spectroscopic survey of X-ray-selected, massive (~10 super( 14)-10 super( 15) M...) galaxy clusters discovered in ROSAT and XMM-Newton imaging. The immediate aim is to determine precise (... ~ 0.001) redshifts for 4000-5000 of these systems out to z ~ 0.6. The scientific goal of the program is precision cosmology, using clusters as probes of large-scale structure in the expanding Universe. We present the cluster samples, target selection algorithms and observation strategies. We demonstrate the efficiency of selecting targets using a combination of SDSS imaging data, a robust red-sequence finder and a dedicated prioritization scheme. We describe a set of algorithms and work-flow developed to collate spectra and assign cluster membership, and to deliver catalogues of spectroscopically confirmed clusters. We discuss the relevance of line-of-sight velocity dispersion estimators for the richer systems. We illustrate our techniques by constructing a catalogue of 230 spectroscopically validated clusters (0.031 < z < 0.658), found in pilot observations. We discuss two potential science applications of the SPIDERS sample: the study of the X-ray luminosity-velocity dispersion (L sub( X)-...) relation and the building of stacked phase-space diagrams. (ProQuest: ... denotes formulae/symbols omitted.)
We compare the Planck Sunyaev–Zeldovich (SZ) cluster sample (PSZ1) to the Sloan Digital Sky Survey (SDSS) redMaPPer catalogue, finding that all Planck clusters within the redMaPPer mask and within ...the redshift range probed by redMaPPer are contained in the redMaPPer cluster catalogue. These common clusters define a tight scaling relation in the richness-SZ mass (λ–M
SZ) plane, with an intrinsic scatter in richness of
$\sigma _{\lambda |M_{{\rm SZ}}} = 0.266 \pm 0.017$
. The corresponding intrinsic scatter in true cluster halo mass at fixed richness is ≈21 per cent. The regularity of this scaling relation is used to identify failures in both catalogues. The failure rates for redMaPPer and PSZ1 1.2 per cent and 14.7 per cent, respectively. The PSZ1 failure rates decreases to 9.8 per cent after removing incorrect redshifts that were drawn from the literature. We note the failure rates in the PSZ1 from this analysis are specific to the SDSS overlap region, and may not be indicative of failure rates over the full Planck survey. We have further identified five PSZ1 sources that suffer from projection effects (multiple rich systems along the line of sight of the SZ detection) and 17 new high-redshift (z ≳ 0.6) cluster candidates of varying degrees of confidence.
The cosmological utility of galaxy cluster catalogues is primarily limited by our ability to calibrate the relation between halo mass and observable mass proxies such as cluster richness, X-ray ...luminosity or the Sunyaev-Zeldovich signal. Projection effects are a particularly pernicious systematic effect that can impact observable mass proxies; structure along the line of sight can both bias and increase the scatter of the observable mass proxies used in cluster abundance studies. In this work, we develop an empirical method to characterize the impact of projection effects on redMaPPer cluster catalogues. We use numerical simulations to validate our method and illustrate its robustness. We demonstrate that modeling of projection effects is a necessary component for cluster abundance studies capable of reaching $\approx 5\%$ mass calibration uncertainties (e.g. the Dark Energy Survey Year 1 sample). Specifically, ignoring the impact of projection effects in the observable--mass relation --- i.e. marginalizing over a log-normal model only --- biases the posterior of the cluster normalization condition $S_8 \equiv \sigma_8 (\Omega_{\rm m}/0.3)^{1/2}$ by $\Delta S_8 =0.05$, more than twice the uncertainty in the posterior for such an analysis.
The primary difficulty in measuring dynamical masses of galaxy clusters from galaxy data lies in the separation between true cluster members from interloping galaxies along the line of sight. We ...study the impact of membership contamination and incompleteness on cluster mass estimates obtained with 25 commonly used techniques applied to nearly 1000 mock clusters with precise spectroscopic redshifts. We show that all methods overestimate or underestimate cluster masses when applied to contaminated or incomplete galaxy samples, respectively. This appears to be the main source of the intrinsic scatter in the mass scaling relation. Applying corrections based on a prior knowledge of contamination and incompleteness can reduce the scatter to the level of shot noise expected for poorly sampled clusters. We establish an empirical model quantifying the effect of imperfect membership on cluster mass estimation and discuss its universal and method-dependent features. We find that both imperfect membership and the response of the mass estimators depend on cluster mass, effectively causing a flattening of the estimated–true mass relation. Imperfect membership thus alters cluster counts determined from spectroscopic surveys, hence the cosmological parameters that depend on such counts.
We examine systematic differences in the derived X-ray properties of galaxy clusters as reported by three different groups: Vikhlinin et al., Mantz et al. and Plank Collaboration. The sample overlap ...between any two pairs of works ranges between 16 to 28 galaxy clusters. We find systematic differences in most reported X-ray properties, including the total cluster mass, M
500. The most extreme case is an average 45 ± 5 per cent difference in cluster mass between the Plank Collaboration and Mantz et al., for clusters at z > 0.13 (averaged over 16 clusters). These differences also induce differences in cluster observables defined within an R
500 aperture. After accounting for aperture differences, we find very good agreement in gas mass estimates between the different groups. However, the soft-band X-ray luminosity, L
X, core-excised spectroscopic temperature, T
X, and gas thermal energy, Y
X = M
gas
T
X display mean differences at the 5-15 per cent level. We also find that the low (z ≤ 0.13) and high (z ≥ 0.13) redshift galaxy cluster samples in Plank Collaboration appear to be systematically different: the Y
SZ/Y
X ratio for each of these two sub-samples is ln (Y
SZ/Y
X) = −0.06 ± 0.04 and ln (Y
SZ/Y
X) = 0.08 ± 0.04, respectively.
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