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Register a new userChandra Follow-Up of the SDSS DR8 redMaPPer Catalog Using the MATCha Pipeline
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In order to place constraints on cosmology through optical surveys of galaxy clusters, one must first understand the properties of those clusters. To this end, we introduce the Mass Analysis Tool for Chandra (MATCha), a pipeline which uses a parallellized algorithm to analyze archival Chandra data. MATCha simultaneously calculates X-ray temperatures and luminosities and performs centering measurements for hundreds of potential galaxy clusters using archival X-ray exposures. We run MATCha on the redMaPPer SDSS DR8 cluster catalog and use MATChas output X-ray temperatures and luminosities to analyze the galaxy cluster temperature-richness, luminosity-richness, luminosity-temperature, and temperature-luminosity scaling relations. We detect 447 clusters and determine 246 r2500 temperatures across all redshifts. Within 0.1 < z < 0.35 we find that r2500 Tx scales with optical richness as ln(kB Tx / 1.0 keV) = (0.52 pm 0.05) ln({lambda}/70) + (1.85 pm 0.03) with intrinsic scatter of 0.27 pm 0.02 (1 {sigma}). We investigate the distribution of offsets between the X-ray center and redMaPPer center within 0.1 < z < 0.35, finding that 68.3 pm 6.5% of clusters are well-centered. However, we find a broad tail of large offsets in this distribution, and we explore some of the causes of redMaPPer miscentering.
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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 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 is superb. We apply the redMaPPer algorithm to ~10,000 deg^2 of SDSS DR8 data, and present the resulting catalog of ~25,000 clusters over the redshift range 0.08<z<0.55. The redMaPPer photometric redshifts are nearly Gaussian, with a scatter sigma_z ~ 0.006 at z~0.1, increasing to sigma_z~0.02 at z~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 (<=5%). Detailed performance comparisons of the redMaPPer DR8 cluster catalog to X-ray and SZ catalogs are presented in a companion paper (Rozo & Rykoff 2014).
We improve upon the cosmological constraints derived from the abundance and weak-lensing data of redMaPPer clusters detected in the Sloan Digital Sky Survey (SDSS). Specifically, we derive gas mass data using Chandra X-ray follow-up of a complete sample of the 30 richest SDSS redMaPPer clusters with $zin[0.1,0.3]$, and use these additional data to improve upon the original analysis by Costanzi et al. (2019b). We simultaneously fit for the parameters of the richness-mass relation, the cluster gas mass-mass relation, and cosmology. By including our X-ray cluster sample in the SDSS cluster cosmology analysis, we measure $Omega_{rm m} = 0.25 pm 0.04$ and $sigma_8 = 0.85^{+0.06}_{-0.08}$. These constraints represent a 25.5% and 29.8% reduction in the size of the 68% confidence intervals of $Omega_{rm m}$ and $sigma_8$ respectively, relative to the constraints published in Costanzi et al. (2019b). Our cosmological constraints are in agreement with early universe results from Planck. As a byproduct of our analysis, we also perform an independent calibration of the amplitude of the $langle M_{rm gas}^{rm true}|M_{rm 500c}rangle$ scaling relation. Our calibration is consistent with and of comparable precision to that of Mantz et al. (2016b).
In the 1-2.5 micron range, spectroscopic observations are made on the AcuA-spec asteroids, whose spectra were obtained in a continuous covered mode between 2.5-5.0 micron by AKARI. Based on the Bus-DeMeo taxonomy (DeMeo et al. 2009, Icarus, 202, 160), all the AcuA-spec asteroids are classified, using the published and our observational data. Additionally, taking advantage of the Bus-DeMeo taxonomy characteristics, we constrain the characteristic each spectral type by combining the taxonomy results with the other physical observational data from colorimetry, polarimetry, radar, and radiometry. As a result, it is suggested that certain C-, Cb-, B-type, dark X-, and D-complex asteroids have spectral properties compatible with those of anhydrous interplanetary dust particles with tiny bright material, such as water ice. This supports the proposal regarding the C-complex asteroids (Vernazza et al. 2015, ApJ, 806, 204; 2017, AJ, 153, 72). A combination of the Bus-DeMeo taxonomy for AcuA-spec asteroids and the presumptions with other physical clues such as the polarimetric inversion angle, radar albedo, and mid-infrared spectroscopic spectra will be beneficial for surface material constraints, from the AcuA-spec asteroid observations.
The accurate determination of the galaxy cluster mass-observable relations is one of the major challenge of modern astrophysics and cosmology. We present a new statistical methodology to constrain the evolution of the mass-observable relations. Instead of measuring individual mass of galaxy clusters, we only consider large scale homogeneity of the Universe. In this case, we expect the present galaxy cluster mass function to be the same everywhere in the Universe. Using relative abundance matching, we contraint the relation between the richness, $lambda(z)$, and the expected present mass, $M(t_0)$, of galaxy clusters. We apply this approach to the redMaPPer galaxy cluster catalogue in 10 redshift bins from $z=0.1$ to $0.6$. We found that the $lambda(z)$-$M(t_0)$ relation is not evolving from $z=0.1$ to $0.4$, whereas it starts to significantly evolve at higher redshift. This results implies that the redMaPPer richness appears to be a better proxy for the expected present-day galaxy cluster mass than for the mass at the observational redshift. Assuming cosmology and galaxy cluster mass accretion history, it is possible to convert $M(t_0)$ to the mass at the galaxy cluster redshift $M(t_z)$. We found a significant evolution of the $lambda(z)$-$M(t_z)$ over all the covered redshift range. Consequently, we provide a new redshift-dependent richness-mass relation for the redMaPPer galaxy cluster catalogue. This results demonstrates the efficiency of this new methodology to probe the evolution of scaling relations compared to individual galaxy cluster mass estimation.
We present a calibration of the fundamental plane using SDSS Data Release 8. We analysed about 93000 elliptical galaxies up to $z<0.2$, the largest sample used for the calibration of the fundamental plane so far. We incorporated up-to-date K-corrections and used GalaxyZoo data to classify the galaxies in our sample. We derived independent fundamental plane fits in all five Sloan filters u, g, r, i and z. A direct fit using a volume-weighted least-squares method was applied to obtain the coefficients of the fundamental plane, which implicitly corrects for the Malmquist bias. We achieved an accuracy of 15% for the fundamental plane as a distance indicator. We provide a detailed discussion on the calibrations and their influence on the resulting fits. These re-calibrated fundamental plane relations form a well-suited anchor for large-scale peculiar-velocity studies in the nearby universe. In addition to the fundamental plane, we discuss the redshift distribution of the elliptical galaxies and their global parameters.
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