Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new user$mu_{star}$ Masses: Weak Lensing Calibration of the Dark Energy Survey Year 1 redMaPPer Clusters using Stellar Masses
74
0
0.0
(
0
)
Added by
Maria Elidaiana Da Silva Pereira
Publication date
2020
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
We present the weak lensing mass calibration of the stellar mass based $mu_{star}$ mass proxy for redMaPPer galaxy clusters in the Dark Energy Survey Year 1. For the first time we are able to perform a calibration of $mu_{star}$ at high redshifts, $z>0.33$. In a blinded analysis, we use $sim 6,000$ clusters split into 12 subsets spanning the ranges $0.1 leqslant z<0.65$ and $mu_{star}$ up to $sim 5.5 times 10^{13} M_{odot}$, and infer the average masses of these subsets through modelling of their stacked weak lensing signal. In our model we account for the following sources of systematic uncertainty: shear measurement and photometric redshift errors, miscentring, cluster-member contamination of the source sample, deviations from the NFW halo profile, halo triaxiality and projection effects. We use the inferred masses to estimate the joint mass--$mu_{star}$--$z$ scaling relation given by $langle M_{200c} | mu_{star},z rangle = M_0 (mu_{star}/5.16times 10^{12} mathrm{M_{odot}})^{F_{mu_{star}}} ((1+z)/1.35)^{G_z}$. We find $M_0= (1.14 pm 0.07) times 10^{14} mathrm{M_{odot}}$ with $F_{mu_{star}}= 0.76 pm 0.06$ and $G_z= -1.14 pm 0.37$. We discuss the use of $mu_{star}$ as a complementary mass proxy to the well-studied richness $lambda$ for: $i)$ exploring the regimes of low $z$, $lambda<20$ and high $lambda$, $z sim 1$; $ii)$ testing systematics such as projection effects for applications in cluster cosmology.
rate research
Read More
We constrain the mass--richness scaling relation of redMaPPer galaxy clusters identified in the Dark Energy Survey Year 1 data using weak gravitational lensing. We split clusters into $4times3$ bins of richness $lambda$ and redshift $z$ for $lambdageq20$ and $0.2 leq z leq 0.65$ and measure the mean masses of these bins using their stacked weak lensing signal. By modeling the scaling relation as $langle M_{rm 200m}|lambda,zrangle = M_0 (lambda/40)^F ((1+z)/1.35)^G$, we constrain the normalization of the scaling relation at the 5.0 per cent level as $M_0 = [3.081 pm 0.075 ({rm stat}) pm 0.133 ({rm sys})] cdot 10^{14} {rm M}_odot$ at $lambda=40$ and $z=0.35$. The richness scaling index is constrained to be $F=1.356 pm 0.051 ({rm stat})pm 0.008 ({rm sys})$ and the redshift scaling index $G=-0.30pm 0.30 ({rm stat})pm 0.06 ({rm sys})$. These are the tightest measurements of the normalization and richness scaling index made to date. We use a semi-analytic covariance matrix to characterize the statistical errors in the recovered weak lensing profiles. Our analysis accounts for the following sources of systematic error: shear and photometric redshift errors, cluster miscentering, cluster member dilution of the source sample, systematic uncertainties in the modeling of the halo--mass correlation function, halo triaxiality, and projection effects. We discuss prospects for reducing this systematic error budget, which dominates the uncertainty on $M_0$. Our result is in excellent agreement with, but has significantly smaller uncertainties than, previous measurements in the literature, and augurs well for the power of the DES cluster survey as a tool for precision cosmology and upcoming galaxy surveys such as LSST, Euclid and WFIRST.
We use weak-lensing shear measurements to determine the mean mass of optically selected galaxy clusters in Dark Energy Survey Science Verification data. In a blinded analysis, we split the sample of more than 8,000 redMaPPer clusters into 15 subsets, spanning ranges in the richness parameter $5 leq lambda leq 180$ and redshift $0.2 leq z leq 0.8$, and fit the averaged mass density contrast profiles with a model that accounts for seven distinct sources of systematic uncertainty: shear measurement and photometric redshift errors; cluster-member contamination; miscentering; deviations from the NFW halo profile; halo triaxiality; and line-of-sight projections. We combine the inferred cluster masses to estimate the joint scaling relation between mass, richness and redshift, $mathcal{M}(lambda,z) varpropto M_0 lambda^{F} (1+z)^{G}$. We find $M_0 equiv langle M_{200mathrm{m}},|,lambda=30,z=0.5rangle=left[ 2.35 pm 0.22 rm{(stat)} pm 0.12 rm{(sys)} right] cdot 10^{14} M_odot$, with $F = 1.12,pm,0.20 rm{(stat)}, pm, 0.06 rm{(sys)}$ and $G = 0.18,pm, 0.75 rm{(stat)}, pm, 0.24 rm{(sys)}$. The amplitude of the mass-richness relation is in excellent agreement with the weak-lensing calibration of redMaPPer clusters in SDSS by Simet et al. (2016) and with the Saro et al. (2015) calibration based on abundance matching of SPT-detected clusters. Our results extend the redshift range over which the mass-richness relation of redMaPPer clusters has been calibrated with weak lensing from $zleq 0.3$ to $zleq0.8$. Calibration uncertainties of shear measurements and photometric redshift estimates dominate our systematic error budget and require substantial improvements for forthcoming studies.
The center determination of a galaxy cluster from an optical cluster finding algorithm can be offset from theoretical prescriptions or $N$-body definitions of its host halo center. These offsets impact the recovered cluster statistics, affecting both richness measurements and the weak lensing shear profile around the clusters. This paper models the centering performance of the RM~cluster finding algorithm using archival X-ray observations of RM-selected clusters. Assuming the X-ray emission peaks as the fiducial halo centers, and through analyzing their offsets to the RM~centers, we find that $sim 75pm 8 %$ of the RM~clusters are well centered and the mis-centered offset follows a Gamma distribution in normalized, projected distance. These mis-centering offsets cause a systematic underestimation of cluster richness relative to the well-centered clusters, for which we propose a descriptive model. Our results enable the DES Y1 cluster cosmology analysis by characterizing the necessary corrections to both the weak lensing and richness abundance functions of the DES Y1 redMaPPer cluster catalog.
We present two galaxy shape catalogues from the Dark Energy Survey Year 1 data set, covering 1500 square degrees with a median redshift of $0.59$. The catalogues cover two main fields: Stripe 82, and an area overlapping the South Pole Telescope survey region. We describe our data analysis process and in particular our shape measurement using two independent shear measurement pipelines, METACALIBRATION and IM3SHAPE. The METACALIBRATION catalogue uses a Gaussian model with an innovative internal calibration scheme, and was applied to $riz$-bands, yielding 34.8M objects. The IM3SHAPE catalogue uses a maximum-likelihood bulge/disc model calibrated using simulations, and was applied to $r$-band data, yielding 21.9M objects. Both catalogues pass a suite of null tests that demonstrate their fitness for use in weak lensing science. We estimate the 1$sigma$ uncertainties in multiplicative shear calibration to be $0.013$ and $0.025$ for the METACALIBRATION and IM3SHAPE catalogues, respectively.
We construct the largest curved-sky galaxy weak lensing mass map to date from the DES first-year (DES Y1) data. The map, about 10 times larger than previous work, is constructed over a contiguous $approx1,500 $deg$^2$, covering a comoving volume of $approx10 $Gpc$^3$. The effects of masking, sampling, and noise are tested using simulations. We generate weak lensing maps from two DES Y1 shear catalogs, Metacalibration and Im3shape, with sources at redshift $0.2<z<1.3,$ and in each of four bins in this range. In the highest signal-to-noise map, the ratio between the mean signal-to-noise in the E-mode and the B-mode map is $sim$1.5 ($sim$2) when smoothed with a Gaussian filter of $sigma_{G}=30$ (80) arcminutes. The second and third moments of the convergence $kappa$ in the maps are in agreement with simulations. We also find no significant correlation of $kappa$ with maps of potential systematic contaminants. Finally, we demonstrate two applications of the mass maps: (1) cross-correlation with different foreground tracers of mass and (2) exploration of the largest peaks and voids in the maps.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
