redMaPPer. I. ALGORITHM AND SDSS DR8 CATALOG

By Eli S. Rykoff, E. Rozo, Michael T. Busha, C. Cunha, A. Finoguenov, August E. Evrard, J. Hao, Benjamin P. Koester, A. Leauthaud, B. Nord and 5 more. 2013

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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 deg2 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 σz ≈ 0.006 at z ≈ 0.1, increasing to σz ≈ 0.02 at z ≈ 0.5 due to increased photometric noise near the survey limit. The median value for |Δz|/(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 Sunyaev–Zel'dovich catalogs are presented in a companion paper.

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/d02840d5d66829a96247f3aa6de3e2ac682ab4eb

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Citations in the corpus

SDSS-III Baryon Oscillation Spectroscopic Survey Data Release 12: galaxy target selection and large-scale structure catalogues (2015, background): “The list of objects is described in section 2.1 of (Rykoff et al. 2014).”
Lensing is Low: Cosmology, Galaxy Formation, or New Physics? (2016, methodology): “10, Rozo & Rykoff 2014; Rykoff et al. 2014) and compare with a fully independent measurement using the SDSS catalogue of Reyes et al. (2012).”
THE ELEVENTH AND TWELFTH DATA RELEASES OF THE SLOAN DIGITAL SKY SURVEY: FINAL DATA FROM SDSS-III (2015, methodology): “The sources are optical counterparts to X-ray clusters selected as faint sources in the ROSAT All-Sky Survey (Voges et al. 1999, 2000a) identified by applying the redMaPPer (Rykoff et al. 2014) cluster finding algorithm to the position of an X-ray source.”
DETECTION OF LENSING SUBSTRUCTURE USING ALMA OBSERVATIONS OF THE DUSTY GALAXY SDP.81 (2016, background): “…catalog, this galaxy is a member (with 87% probability) of a more massive system, with richness λ = 32, where λ corresponds to the number of red sequence galaxies brighter than 0.4 L?. Assuming the mass– richness relationship of Rykoff et al. (2014), this corresponds to a mass of ∼ 2.6× 1014M .”
Dark Energy Survey Year 1 results: weak lensing mass calibration of redMaPPer galaxy clusters (2018, methodology): “We use galaxy clusters identified using the redMaPPer algorithm (Rykoff et al. 2014), which assigns each cluster a photometric redshift and optical richness λ of red galaxies.”
The Simons Observatory: science goals and forecasts (2018, methodology): “…to apply component separation methods to extract maps of the Compton-y signal over large sky areas using the known tSZ spectral function, as has been possible recently for the first time with Planck (Planck Collaboration 2014d; Hill and Spergel 2014; Planck Collaboration 2016h; Khatri 2016).”
THE SLOAN DIGITAL SKY SURVEY COADD: 275 deg2 OF DEEP SLOAN DIGITAL SKY SURVEY IMAGING ON STRIPE 82 (2011, methodology): “The MaxBCG (Koester et al. 2007) cluster catalog overlaps with Stripe 82 using single pass data (as do other cluster catalogs, e.g., Geach et al. 2011; Dong 2011; Szabo et al. 2011; Rykoff et al. 2014).”
The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample: First measurement of baryon acoustic oscillations between redshift 0.8 and 2.2 (2017, background): “…(Høg et al. 2000; excluding 1.8 per cent of the area); (iii) Bright objects, including, e.g. stars not in the Tycho catalogue and bright galaxies (Rykoff et al. 2014; excluding 0.05 per cent of the area); (iv) Centerposts, which anchor the spectrographic plates and prevent any fibers from being…”
EIGHT ULTRA-FAINT GALAXY CANDIDATES DISCOVERED IN YEAR TWO OF THE DARK ENERGY SURVEY (2015, result): “We also compared against catalogs of other astrophysical objects that produce false positives, such as large nearby galaxies (Nilson 1973; Corwin 2004) and galaxy clusters (Rykoff et al. 2014).”
Neutrino Mass Ordering from Oscillations and Beyond: 2018 Status and Future Prospects (2018, background): “…e.g., Hilton et al., 2018; Sohn et al., 2018 and references therein), the optically detected SDSS photometric redMaPPer cluster catalog (Rozo and Rykoff, 2014; Rykoff et al., 2014; Rozo et al., 2015) and the Planck SZ galaxy clusters (PSZ2) Ade et al. (2016a), which contains more than a…”
The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: observational systematics and baryon acoustic oscillations in the correlation function (2016, background): “These include masks for bright stars, bright objects (Rykoff et al. 2014), and nonphotometric conditions.”
GALAXY CLUSTERS DISCOVERED VIA THE SUNYAEV–ZEL'DOVICH EFFECT IN THE 2500-SQUARE-DEGREE SPT-SZ SURVEY (2014, background): “Clusters identified in this manner typically contain of tens to thousands of galaxies (e.g., Abell 1958; Gladders & Yee 2000; Koester et al. 2007; Eisenhardt et al. 2008; Wen et al. 2012; Rykoff et al. 2014).”
The first and second data releases of the Kilo-Degree Survey (2015, methodology): “…and after (right) the KiDS magnitude cuts were applied: it can be seen that a KiDS-like survey can probe galaxy clusters in the z ∼ 0.4−0.8 redshift range, extending the cluster detection studies based on SDSS data, which are incomplete beyond z ∼ 0.35 (e.g. Rykoff et al. 2014; Rozo et al. 2014).”
Weak Lensing Measurement of the Mass--Richness Relation of SDSS redMaPPer Clusters (2016, methodology): “redMaPPer is a red-sequence photometric cluster finding algorithm (Rykoff et al. 2014, hereafter RM I), built around the optimized richness estimator developed in Rozo et al. (2009b) and Rykoff et al. (2012).”
THE REDMAPPER GALAXY CLUSTER CATALOG FROM DES SCIENCE VERIFICATION DATA (2016, unknown): “In 2014, we in- ? erykoff@slac.stanford.edu 1 http://www.naoj.org/Projects/HSC/HSCProject.html 2 http://wfirst.gsfc.nasa.gov/ troduced the red-sequence matched-filter Probabalistic Percolation cluster finder (redMaPPer; Rykoff et al. 2014, henceforth RM1).”