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.
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.
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.
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.
Reducing the scatter between cluster mass and optical richness is a key goal for cluster cosmology from photometric catalogs. We consider various modifications to the red-sequence-matched filter ...richness estimator of Rozo et al. implemented on the maxBCG cluster catalog and evaluate the impact of these changes on the scatter in X-ray luminosity (L sub(X)) at fixed richness, using L sub(X) from the ROSAT All-Sky Catalog as the best mass proxy available for the large area required. Most significantly, we find that deeper luminosity cuts can reduce the recovered scatter, finding that sigma sub(lnLX|lambda) = 0.63+ or -0.02 for clusters with M sub(500c) > ~ 1.6 x 10 super(14) h sub(70) super(-1) M sub(middot in circle). The corresponding scatter in mass at fixed richness is sigma sub(ln M|lambda) approx = 0.2-0.3 depending on the richness, comparable to that for total X-ray luminosity. We find that including blue galaxies in the richness estimate increases the scatter, as does weighting galaxies by their optical luminosity. We further demonstrate that our richness estimator is very robust. Specifically, the filter employed when estimating richness can be calibrated directly from the data, without requiring a priori calibrations of the red sequence. We also demonstrate that the recovered richness is robust to up to 50% uncertainties in the galaxy background, as well as to the choice of photometric filter employed, so long as the filters span the 4000 A break of red-sequence galaxies. Consequently, our richness estimator can be used to compare richness estimates of different clusters, even if they do not share the same photometric data. Appendix A includes "easy-bake" instructions for implementing our optimal richness estimator, and we are releasing an implementation of the code that works with Sloan Digital Sky Survey data, as well as an augmented maxBCG catalog with the lambda richness measured for each cluster.
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 compare cluster scaling relations published for three different samples selected via X-ray and Sunyaev-Zel'dovich (SZ) signatures. We find tensions driven mainly by two factors: (i) systematic ...differences in the X-ray cluster observables used to derive the scaling relations and (ii) uncertainty in the modelling of how the gas mass of galaxy clusters scales with total mass. All scaling relations are in agreement after accounting for these two effects. We describe a multivariate scaling model that enables a fully self-consistent treatment of multiple observational catalogues in the presence of property covariance and apply this formalism when interpreting published results. The corrections due to scatter and observable covariance can be significant. For instance, our predicted Y
SZ-LX
scaling relation differs from that derived using the naive 'plug in' method by 25 per cent. Finally, we test the mass normalization for each of the X-ray data sets we consider by applying a space density consistency test: we compare the observed ROSAT-ESO Flux-Limited X-ray (REFLEX) luminosity function to expectations from published L
X-M relations convolved with the mass function for a Wilkinson Microwave Anisotropy Probe 7 flat Λ cold dark matter model.
The Dark Energy Survey Image Processing Pipeline Morganson, E.; Gruendl, R. A.; Menanteau, F. ...
Publications of the Astronomical Society of the Pacific,
07/2018, Letnik:
130, Številka:
989
Journal Article
Recenzirano
Odprti dostop
The Dark Energy Survey (DES) is a five-year optical imaging campaign with the goal of understanding the origin of cosmic acceleration. DES performs a ∼5000 deg2 survey of the southern sky in five ...optical bands (g, r, i, z, Y) to a depth of ∼24th magnitude. Contemporaneously, DES performs a deep, time-domain survey in four optical bands (g, r, i, z) over ∼27 deg2. DES exposures are processed nightly with an evolving data reduction pipeline and evaluated for image quality to determine if they need to be retaken. Difference imaging and transient source detection are also performed in the time domain component nightly. On a bi-annual basis, DES exposures are reprocessed with a refined pipeline and coadded to maximize imaging depth. Here we describe the DES image processing pipeline in support of DES science, as a reference for users of archival DES data, and as a guide for future astronomical surveys.
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.
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