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Register a new userRemoving the Impact of Correlated PSF Uncertainties in Weak Lensing
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Accurate reconstruction of the spatial distributions of the Point Spread Function (PSF) is crucial for high precision cosmic shear measurements. Nevertheless, current methods are not good at recovering the PSF fluctuations of high spatial frequencies. In general, the residual PSF fluctuations are spatially correlated, therefore can significantly contaminate the correlation functions of the weak lensing signals. We propose a method to correct for this contamination statistically, without any assumptions on the PSF and galaxy morphologies or their spatial distribution. We demonstrate our idea with the data from the W2 field of CFHTLenS.
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Reconstruction of the point spread function (PSF) is a critical process in weak lensing measurement. We develop a real-data based and galaxy-oriented pipeline to compare the performances of various PSF reconstruction schemes. Making use of a large amount of the CFHTLenS data, the performances of three classes of interpolating schemes - polynomial, Kriging, and Shepard - are evaluated. We find that polynomial interpolations with optimal orders and domains perform the best. We quantify the effect of the residual PSF reconstruction error on shear recovery in terms of the multiplicative and additive biases, and their spatial correlations using the shear measurement method of Zhang et al. (2015). We find that the impact of PSF reconstruction uncertainty on the shear-shear correlation can be significantly reduced by cross correlating the shear estimators from different exposures. It takes only 0.2 stars (SNR > 100) per square arcmin on each exposure to reach the best performance of PSF interpolation, a requirement that is generally satisfied in the CFHTlenS data.
We improve the ERA(Ellipticity of Re-smeared Artificial image) method of PSF(Point Spread Function) correction in weak lensing shear analysis in order to treat realistic shape of galaxies and PSF. This is done by re-smearing PSF and the observed galaxy image smeared by a RSF(Re-Smearing Function), and allows us to use a new PSF with a simple shape and to correct PSF effect without any approximations and assumptions. We perform numerical test to show that the method applied for galaxies and PSF with some complicated shapes can correct PSF effect with systematic error less than 0.1%. We also apply ERA method for real data of Abell 1689 cluster to confirm that it is able to detect the systematic weak lensing shear pattern. The ERA method requires less than 0.1 or 1 second to correct PSF for each object in numerical test and real data analysis, respectively.
The robust estimation of the tiny distortions (shears) of galaxy shapes caused by weak gravitational lensing in the presence of much larger shape distortions due to the point-spread function (PSF) has been widely investigated. One major problem is that most galaxy shape measurement methods are subject to bias due to pixel noise in the images (noise bias). Noise bias is usually characterized using uncorrelated noise fields; however, real images typically have low-level noise correlations due to galaxies below the detection threshold, and some types of image processing can induce further noise correlations. We investigate the effective detection significance and its impact on noise bias in the presence of correlated noise for one method of galaxy shape estimation. For a fixed noise variance, the biases in galaxy shape estimates can differ substantially for uncorrelated versus correlated noise. However, use of an estimate of detection significance that accounts for the noise correlations can almost entirely remove these differences, leading to consistent values of noise bias as a function of detection significance for correlated and uncorrelated noise. We confirm the robustness of this finding to properties of the galaxy, the PSF, and the noise field, and quantify the impact of anisotropy in the noise correlations. Our results highlight the importance of understanding the pixel noise model and its impact on detection significances when correcting for noise bias on weak lensing.
We generalize ERA method of PSF correction for more realistic situations. The method re-smears the observed galaxy image(galaxy image smeared by PSF) and PSF image by an appropriate function called Re-Smearing Function(RSF) to make new images which have the same ellipticity with the lensed (before smeared by PSF) galaxy image. It has been shown that the method avoids a systematic error arising from an approximation in the usual PSF correction in moment method such as KSB for simple PSF shape. By adopting an idealized PSF we generalize ERA method applicable for arbitrary PSF. This is confirmed with simulated complex PSF shapes. We also consider the effect of pixel noise and found that the effect causes systematic overestimation.
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.
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