ROBUST OPTICAL RICHNESS ESTIMATION WITH REDUCED SCATTER

By Eli S. Rykoff, Benjamin P. Koester, E. Rozo, James Annis, August E. Evrard, S. Hansen, J. Hao, D. Johnston, T. McKay, Risa Wechsler. 2011

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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 (LX) at fixed richness, using LX 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 for clusters with M500c ≳ 1.6 × 1014 h−170 M☉. The corresponding scatter in mass at fixed richness is σln M|λ ≈ 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 Å 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 λ richness measured for each cluster.

Subfields: Astronomy and Astrophysics, Instrumentation

View Paper: https://www.semanticscholar.org/paper/47020985d9ed1c44e059cbfc31437996ec84fe9c

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Citations in the corpus

Stellar Mass—Halo Mass Relation and Star Formation Efficiency in High-Mass Halos (2014, background): “Using a larger sample, we find a larger scatter of ≈ 29%, comparable to scatter of total X-ray luminosity (without core excision) or richness (e.g., Rykoff et al. 2012) at a fixed mass, but several times larger than scatter in gas mass and YX (e.g., Kravtsov et al. 2006; Nagai et al. 2007).”
COSMOLIKE - cosmological likelihood analyses for photometric galaxy surveys (2016, methodology): “Cluster mass-observable relation, the fiducial values for the massrichness relation parameters (Cλ, αλ) are adopted from equation (B4) in Rykoff et al. (2012), transformed to h100 units; we furthermore use βλ = 0 and σ lnM|λ = 0.25 as baseline value.”
Dark Energy Survey Year 1 results: weak lensing mass calibration of redMaPPer galaxy clusters (2018, background): “This relation was found to minimize the scatter between richness and X-ray luminosity in Rykoff et al. (2012).”
Scaling relations for galaxy clusters in the Millennium-XXL simulation (2012, result): “The values we find are consistent with previous studies, but note that our estimators are not optimal (c.f. Melin et al. 2006; Rykoff et al. 2012).”
Weak Lensing Measurement of the Mass--Richness Relation of SDSS redMaPPer Clusters (2016, methodology): “There is an apparent slight tension (2.5σ) in the value of the slope, but Rykoff et al. (2012) did not report an uncertainty in the slope.”
Observational probes of cosmic acceleration (2012, methodology): “This discussion demonstrates the value of finding improved optical richness estimators that have lower scatter relative to mass (Rozo et al., 2009; Rykoff et al., 2012).”
A CATALOG OF 132,684 CLUSTERS OF GALAXIES IDENTIFIED FROM SLOAN DIGITAL SKY SURVEY III (2012, result): “The scatter is in agreement with that for the correlation ofLX with the maxBCG richness (Rykoff et al. 2008), but larger than those for the correlations with the improved maxBCG richness (Rozo et al. 10 2009b; Rykoff et al. 2012).”
Milky Way Satellite Census. I. The Observational Selection Function for Milky Way Satellites in DES Y3 and Pan-STARRS DR1 (2019, methodology): “…formalism (e.g., Orear 1958; Barlow 1990) is widely used in physics and astronomy as a means to perform statistical searches for overdensities in a Poisson random field with unknown signal normalization (e.g., galaxy cluster searches as described in Kepner et al. 1999 or Rykoff et al. 2012).”
THE REDMAPPER GALAXY CLUSTER CATALOG FROM DES SCIENCE VERIFICATION DATA (2016, methodology): “Furthermore, the parameters of the λ– mass relation are consistent with what was derived from SDSS DR8 data using a rough abundance matching argument (Rykoff et al. 2012a), thus giving further confirmation to the fact that redMaPPer is probing a similar cluster population in SDSS and DES data.”
GALAXY CLUSTERS DISCOVERED VIA THE SUNYAEV–ZEL'DOVICH EFFECT IN THE 2500-SQUARE-DEGREE SPT-SZ SURVEY (2014, methodology): “We compare our stellar-synthesis m∗(z) model to the Rykoff et al. (2012) model for the maxBCG cluster sample over the redshift range for which that model is valid (0.05 < z < 0.35).”
redMaPPer. I. ALGORITHM AND SDSS DR8 CATALOG (2013, background): “Concerning the last point in particular, over the past several years we have empirically explored what works and what does not work in estimating cluster richness (Rozo et al. 2009, 2011; Rykoff et al. 2012, henceforth R12).”