Related papers: Dependence of cosmic shear covariances on cosmolog…
Super sample covariance (SSC) is important when estimating covariance matrices using a set of mock catalogues for galaxy surveys. If the underlying cosmological simulations do not include the variation in background parameters appropriate…
The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of…
Photometric galaxy surveys probe the late-time Universe where the density field is highly non-Gaussian. A consequence is the emergence of the super-sample covariance (SSC), a non-Gaussian covariance term that is sensitive to fluctuations on…
(Abridged) Combining cosmic shear power spectra and cluster counts is powerful to improve cosmological parameter constraints and/or test inherent systematics. However they probe the same cosmic mass density field, if the two are drawn from…
The results from weak gravitational lensing analyses are subject to a cosmic variance error term that has previously been estimated assuming Gaussian statistics. In this letter we address the issue of estimating cosmic variance errors for…
The weak lensing shear signal has been measured numerically in $N$-body simulations at 14 different redshifts ($z_s = 0.1$ to 3.6) and on angular scales of $\theta = 2'$ to 32'. In addition, the data have been validated by analytical…
We compute covariance matrices for many observed estimates of the stellar mass function of galaxies from $z=0$ to $z\approx 4$, and for one estimate of the projected correlation function of galaxies split by stellar mass at $z\lesssim 0.5$.…
Cosmological covariance matrices are fundamental for parameter inference, since they are responsible for propagating uncertainties from the data down to the model parameters. However, when data vectors are large, in order to estimate…
When inferring parameters from a Gaussian-distributed data set by computing a likelihood, a covariance matrix is needed that describes the data errors and their correlations. If the covariance matrix is not known a priori, it may be…
We show that the lensing efficiency of cosmic shear generically has a simple shape, even in the case of a tomographic survey with badly behaved photometric redshifts. We argue that source distributions for cosmic shear can therefore be more…
The correlation between cosmic shear as measured by the image distortion of high-redshift galaxies and the number counts of foreground galaxies is calculated. For a given power spectrum of the cosmic density fluctuations, this correlation…
We study the significance of non-Gaussianity in the likelihood of weak lensing shear two-point correlation functions, detecting significantly non-zero skewness and kurtosis in one-dimensional marginal distributions of shear two-point…
The forecasted accuracy of upcoming surveys of large-scale structure cannot be achieved without a proper quantification of the error induced by foreground removal (or other systematics like 0-point photometry offset). Because these errors…
We propose to use a simple observable, the fractional area of "hot spots" in weak lensing mass maps which are detected with high significance, to determine background cosmological parameters. Because these high-shear regions are directly…
Analytical expressions for covariances of weak lensing statistics related to the aperture mass $\Map$ are derived for realistic survey geometries such as SNAP for a range of smoothing angles and redshift bins. We incorporate the…
It is necessary to make assumptions in order to derive models to be used for cosmological predictions and comparison with observational data. In particular, in standard cosmology the spatial curvature is assumed to be constant and zero (or…
The statistical analysis of the lensing effects coupled with the statistical analysis of the number counts is a tool to probe directly the relation between the mass and the light. In particular, some properties of the bias parameter can be…
Accurate and precise covariance matrices will be important in enabling planned cosmological surveys to detect new physics. Standard methods imply either the need for many N-body simulations in order to obtain an accurate estimate, or a…
Stage-IV galaxy surveys will measure correlations at small cosmological scales with high signal-to-noise ratio. One of the main challenges of extracting information from small scales is devising accurate models, as well as characterizing…
Covariance matrix estimation is a persistent challenge for cosmology. We focus on a class of model covariance matrices that can be generated with high accuracy and precision, using a tiny fraction of the computational resources that would…