Abstract
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.
Highlights
Gravitational lensing, the deflection of light by mass, has been used for many different applications due to its sensitivity to all gravitating mass, including dark matter
The second question is whether one can use simulations with uncorrelated noise to calibrate noise bias even for data with correlated noise after accounting for the way correlated noise modifies the signal-to-noise ratio (SNR) of the object detections? Third, should such corrections use the SNR of the galaxy size, flux, or shape? if correlated noise has some directionality, how large is the expected bias (Mandelbaum et al 2012) in shear estimates? We address these questions using GalSim (Rowe et al 2015), an opensource1 image simulation package that was designed for tests of weak lensing measurement algorithms
We have investigated the impact of correlated noise fields on galaxy shape estimation using the re-Gaussianization galaxy shape estimation method
Summary
Gravitational lensing, the deflection of light by mass, has been used for many different applications due to its sensitivity to all gravitating mass, including dark matter. The noise model in astronomical images is typically a combination of Poisson noise on the pixel counts plus other sources of noise that are Gaussian (e.g., read noise). The noise can commonly be modeled as stationary on the scale of galaxy images, at least for the faint galaxies that dominate weak lensing measurements. In this regime, the variance is the same in each pixel, which is true when the Poisson noise on the sky level dominates and when the sky level does not vary much across each galaxy. The noise is largely uncorrelated between pixels, though
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