156 research outputs found
redMaPPer III: A Detailed Comparison of the Planck 2013 and SDSS DR8 RedMaPPer Cluster Catalogs
We compare the Planck Sunyaev-Zeldovich (SZ) cluster sample (PSZ1) to the
Sloan Digital Sky Survey (SDSS) redMaPPer catalog, finding that all Planck
clusters within the redMaPPer mask and within the redshift range probed by
redMaPPer are contained in the redMaPPer cluster catalog. These common clusters
define a tight scaling relation in the richness-SZ mass (--)
plane, with an intrinsic scatter in richness of . The corresponding intrinsic scatter in true cluster halo mass
at fixed richness is . The regularity of this scaling relation is
used to identify failures in both the redMaPPer and Planck cluster catalogs. Of
the 245 galaxy clusters in common, we identify three failures in redMaPPer and
36 failures in the PSZ1. Of these, at least 12 are due to clusters whose
optical counterpart was correctly identified in the PSZ1, but where the quoted
redshift for the optical counterpart in the external data base used in the PSZ1
was incorrect. The failure rates for redMaPPer and the PSZ1 are and
respectively, or 9.8% in the PSZ1 after subtracting the external data
base errors. We have further identified 5 PSZ1 sources that suffer from
projection effects (multiple rich systems along the line-of-sight of the SZ
detection) and 17 new high redshift () cluster candidates of
varying degrees of confidence. Should all of the high-redshift cluster
candidates identified here be confirmed, we will have tripled the number of
high redshift Planck clusters in the SDSS region. Our results highlight the
power of multi-wavelength observations to identify and characterize systematic
errors in galaxy cluster data sets, and clearly establish photometric data both
as a robust cluster finding method, and as an important part of defining clean
galaxy cluster samples.Comment: comments welcom
redMaPPer II: X-ray and SZ Performance Benchmarks for the SDSS Catalog
We evaluate the performance of the SDSS DR8 redMaPPer photometric cluster
catalog by comparing it to overlapping X-ray and SZ selected catalogs from the
literature. We confirm the redMaPPer photometric redshifts are nearly unbiased
( < 0.005), have low scatter (\sigma_z ~ 0.006-0.02, depending on
redshift), and have a low catastrophic failure rate (~ 1%). Both the
T_X-\lambda\ and Mgas-\lambda\ scaling relations are consistent with a mass
scatter of \sigma_{\ln M|\lambda} ~ 25%, albeit with a ~ 1% outlier rate due to
projection effects. 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 < 0.35), the
catalog is 100% complete above T_X > 3.5 keV, and L_X > 2\times 10^{44} erg/s,
decreasing to 90% completeness at L_X ~ 10^{43} erg/s. All rich (\lambda >
100), low redshift (z < 0.25) redMaPPer clusters are X-ray detected in the
ROSAT All Sky Survey (RASS), 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,
with only one exception which performs worse.Comment: comments welcom
Constraining the Mass-Richness Relationship of redMaPPer Clusters with Angular Clustering
The potential of using cluster clustering for calibrating the mass-observable
relation of galaxy clusters has been recognized theoretically for over a
decade. Here, we demonstrate the feasibility of this technique to achieve high
precision mass calibration using redMaPPer clusters in the Sloan Digital Sky
Survey North Galactic Cap. By including cross-correlations between several
richness bins in our analysis we significantly improve the statistical
precision of our mass constraints. The amplitude of the mass-richness relation
is constrained to 7% statistical precision. However, the error budget is
systematics dominated, reaching an 18% total error that is dominated by
theoretical uncertainty in the bias-mass relation for dark matter halos. We
perform a detailed treatment of the effects of assembly bias on our analysis,
finding that the contribution of such effects to our parameter uncertainties is
somewhat greater than that of measurement noise. We confirm the results from
Miyatake et al. (2015) that the clustering amplitude of redMaPPer clusters
depends on galaxy concentration, and provide additional evidence in support of
this effect being due to some form of assembly bias. The results presented here
demonstrate the power of cluster clustering for mass calibration and cosmology
provided the current theoretical systematics can be ameliorated.Comment: 18 pages, 9 figure
Galaxy Cluster Mass Estimation from Stacked Spectroscopic Analysis
We use simulated galaxy surveys to study: i) how galaxy membership in
redMaPPer clusters maps to the underlying halo population, and ii) the accuracy
of a mean dynamical cluster mass, , derived from stacked
pairwise spectroscopy of clusters with richness . Using galaxy pairs patterned after the SDSS redMaPPer cluster sample study
of Rozo et al. (2015 RMIV), we show that the pairwise velocity PDF of
central--satellite pairs with in the simulation matches the form
seen in RMIV. Through joint membership matching, we deconstruct the main
Gaussian velocity component into its halo contributions, finding that the
top-ranked halo contributes of the stacked signal. The halo mass
scale inferred by applying the virial scaling of Evrard et al. (2008) to the
velocity normalization matches, to within a few percent, the log-mean halo mass
derived through galaxy membership matching. We apply this approach, along with
mis-centering and galaxy velocity bias corrections, to estimate the log-mean
matched halo mass at of SDSS redMaPPer clusters. Employing the velocity
bias constraints of Guo et al. (2015), we find with and .
Systematic uncertainty in the velocity bias of satellite galaxies
overwhelmingly dominates the error budget.Comment: 14 pages, 7 figure
Intrinsic Alignment in redMaPPer clusters -- II. Radial alignment of satellites toward cluster centers
We study the orientations of satellite galaxies in redMaPPer clusters
constructed from the Sloan Digital Sky Survey at to determine
whether there is any preferential tendency for satellites to point radially
toward cluster centers. We analyze the satellite alignment (SA) signal based on
three shape measurement methods (re-Gaussianization, de Vaucouleurs, and
isophotal shapes), which trace galaxy light profiles at different radii. The
measured SA signal depends on these shape measurement methods. We detect the
strongest SA signal in isophotal shapes, followed by de Vaucouleurs shapes.
While no net SA signal is detected using re-Gaussianization shapes across the
entire sample, the observed SA signal reaches a statistically significant level
when limiting to a subsample of higher luminosity satellites. We further
investigate the impact of noise, systematics, and real physical isophotal
twisting effects in the comparison between the SA signal detected via different
shape measurement methods. Unlike previous studies, which only consider the
dependence of SA on a few parameters, here we explore a total of 17 galaxy and
cluster properties, using a statistical model averaging technique to naturally
account for parameter correlations and identify significant SA predictors. We
find that the measured SA signal is strongest for satellites with the following
characteristics: higher luminosity, smaller distance to the cluster center,
rounder in shape, higher bulge fraction, and distributed preferentially along
the major axis directions of their centrals. Finally, we provide physical
explanations for the identified dependences, and discuss the connection to
theories of SA.Comment: 25 pages, 16 figures, 7 tables, accepted to MNRAS. Main statistical
analysis tool changed, with the results remain simila
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