Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userWeak lensing mass modeling bias and the impact of miscentring
88
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Parametric modeling of galaxy cluster density profiles from weak lensing observations leads to a mass bias, whose detailed understanding is critical in deriving accurate mass-observable relations for constraining cosmological models. Drawing from existing methods, we develop a robust framework for calculating this mass bias in one-parameter fits to simulations of dark matter halos. We show that our approach has the advantage of being independent of the absolute noise level, so that only the number of halos in a given simulation and the representativeness of the simulated halos for real clusters limit the accuracy of the bias estimation. While we model the bias as a log-normal distribution and the halos with a Navarro-Frenk-White profile, our method can be generalized to any bias distribution and parametric model of the radial mass distribution. We find that the log-normal assumption is not strictly valid in the presence of miscentring of halos. We investigate the use of cluster centers derived from weak lensing in the context of mass bias, and tentatively find that such centroids can yield sensible mass estimates if the convergence peak has a signal-to-noise ratio approximately greater than four. In this context we also find that the standard approach to estimating the positional uncertainty of weak lensing mass peaks using bootstrapping severely underestimates the true positional uncertainty for peaks with low signal-to-noise ratios. Though we determine the mass and redshift dependence of the bias distribution for a few experimental setups, our focus remains providing a general approach to computing such distributions.
rate research
Read More
We study a sample of ~10^4 galaxy clusters in the redshift range 0.2<z<0.8 with masses M_200 > 5x10^13 h_70^-1 M_sun, discovered in the second Red-sequence Cluster Survey (RCS2). The depth and excellent image quality of the RCS2 enable us to detect the cluster-mass cross-correlation up to z~0.7. To obtain cluster masses, concentrations and halo biases, we fit a cluster halo model simultaneously to the lensing signal and to the projected density profile of red-sequence cluster members, as the latter provides tight constraints on the cluster miscentring distribution. We parametrise the mass-richness relation as M_200 = A x (N_200/20)^alpha, and find A = (15.0 +- 0.8) x 10^13 h_70^-1 M_sun and alpha = 0.73 +- 0.07 at low redshift (0.2<z<0.35). At intermediate redshift (0.35<z<0.55), we find a higher normalisation, which points at a fractional increase of the richness towards lower redshift caused by the build-up of the red-sequence. The miscentring distribution is well constrained. Only ~30% of our BCGs coincide with the peak of the dark matter distribution. The distribution of the remaining BCGs are modelled with a 2D-Gaussian, whose width increases from 0.2 to 0.4 h_70^-1 Mpc towards higher masses; the ratio of width and r_200 is constant with mass and has an average value of 0.44 +- 0.01. The mass-concentration and mass-bias relation agree fairly well with literature results at low redshift, but have a higher normalisation at higher redshifts, which may be due to selection and projection effects. The concentration of the satellite distribution decreases with mass and is correlated with the concentration of the halo.
(Abridged) We quantify the bias and scatter in galaxy cluster masses and concentrations derived from an idealised mock weak gravitational lensing (WL) survey, and their effect on the cluster mass-concentration relation. For this, we simulate WL distortions on a population of background galaxies due to a large (~3000) sample of galaxy cluster haloes extracted from the Millennium Simulation at z~0.2. This study takes into account the influence of shape noise, cluster substructure and asphericity as well as correlated large-scale structure, but not uncorrelated large-scale structure along the line of sight and observational effects. We find a small, but non-negligble, negative median bias in both mass and concentration at a level of ~5%, the exact value depending both on cluster mass and radial survey range. Both the mass and concentration derived from WL show considerable scatter about their true values. This scatter has, even for the highest mass clusters of M200 > 10^14.8 M_sun, a level of ~30% and ~20% for concentration and mass respectively and increases strongly with decreasing cluster mass. For a typical survey analysing 30 galaxies per arcmin^2 over a radial range from 30 to 15 from the cluster centre, the derived M200-c relation has a slope and normalisation too low compared to the underlying true (3D) relation by ~40% and ~15% respectively. The scatter and bias in mass are shown to reflect a departure at large radii of the true WL shear/matter distribution of the simulated clusters from the NFW profile adopted in modelling the mock observations. Orientation of the triaxial cluster haloes dominates the concentration scatter (except at low masses, where galaxy shape noise becomes dominant), while the bias in c is mostly due to substructure within the virial radius.
Weak lensing data follow a naturally skewed distribution, implying the data vector most likely yielded from a survey will systematically fall below its mean. Although this effect is qualitatively known from CMB-analyses, correctly accounting for it in weak lensing is challenging, as a direct transfer of the CMB results is quantitatively incorrect. While a previous study (Sellentin et al. 2018) focused on the magnitude of this bias, we here focus on the frequency of this bias, its scaling with redshift, and its impact on the signal-to-noise of a survey. Filtering weak lensing data with COSEBIs, we show that weak lensing likelihoods are skewed up until $ell approx 100$, whereas CMB-likelihoods Gaussianize already at $ell approx 20$. While COSEBI-compressed data on KiDS- and DES-like redshift- and angular ranges follow Gaussian distributions, we detect skewness at 6$sigma$ significance for half of a Euclid- or LSST-like data set, caused by the wider coverage and deeper reach of these surveys. Computing the signal-to-noise ratio per data point, we show that precisely the data points of highest signal-to-noise are the most biased. Over all redshifts, this bias affects at least 10% of a surveys total signal-to-noise, at high redshifts up to 25%. The bias is accordingly expected to impact parameter inference. The bias can be handled by developing non-Gaussian likelihoods. Otherwise, it could be reduced by removing the data points of highest signal-to-noise.
Weak-lensing measurements of the masses of galaxy clusters are commonly based on the assumption of spherically symmetric density profiles. Yet, the cold dark matter model predicts the shapes of dark matter halos to be triaxial. Halo triaxiality, and the orientation of the major axis with respect to the line of sight, are expected to be the leading cause of intrinsic scatter in weak-lensing mass measurements. The shape of central cluster galaxies (Brightest Cluster Galaxies; BCGs) is expected to follow the shape of the dark matter halo. Here we investigate the use of BCG ellipticity as predictor of the weak-lensing mass bias in individual clusters compared to the mean. Using weak lensing masses $M^{rm WL}_{500}$ from the Weighing the Giants project, and $M_{500}$ derived from gas masses as low-scatter mass proxy, we find that, on average, the lensing masses of clusters with the roundest / most elliptical 25% of BCGs are biased $sim 20$% high / low compared to the average, as qualitatively predicted by the cold dark matter model. For cluster cosmology projects utilizing weak-lensing mass estimates, the shape of the BCG can thus contribute useful information on the effect of orientation bias in weak lensing mass estimates as well as on cluster selection bias.
Cosmological inference from cluster number counts is systematically limited by the accuracy of the mass calibration, i.e. the empirical determination of the mapping between cluster selection observables and halo mass. In this work we demonstrate a method to quantitatively determine the bias and uncertainties in weak-lensing mass calibration. To this end, we extract a library of projected matter density profiles from hydrodynamical simulations. Accounting for shear bias and noise, photometric redshift uncertainties, mis-centering, cluster member contamination, cluster morphological diversity, and line-of-sight projections, we produce a library of shear profiles. Fitting a one-parameter model to these profiles, we extract the so-called emph{weak lensing mass} $M_text{WL}$. Relating the weak-lensing mass to the halo mass from gravity-only simulations with the same initial conditions as the hydrodynamical simulations allows us to estimate the impact of hydrodynamical effects on cluster number counts experiments. Creating new shear libraries for $sim$1000 different realizations of the systematics, provides a distribution of the parameters of the weak-lensing to halo mass relation, reflecting their systematic uncertainty. This result can be used as a prior for cosmological inference. We also discuss the impact of the inner fitting radius on the accuracy, and determine the outer fitting radius necessary to exclude the signal from neighboring structures. Our method is currently being applied to different Stage~III lensing surveys, and can easily be extended to Stage~IV lensing surveys.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
