Lance Miller

Abstract:

We present SKiLLS, a suite of multi-band image simulations for the weak lensing analysis of the complete Kilo-Degree Survey (KiDS), dubbed KiDS-Legacy analysis. The resulting catalogues enable joint shear and redshift calibration, enhancing the realism and hence accuracy over previous efforts. To create a large volume of simulated galaxies with faithful properties and to a sufficient depth, we integrated cosmological simulations with high-quality imaging observations. We also improved the realism of simulated images by allowing the point spread function (PSF) to differ between CCD images, including stellar density variations and varying noise levels between pointings. Using realistic variable shear fields, we accounted for the impact of blended systems at different redshifts. Although the overall correction is minor, we found a clear redshift-bias correlation in the blending-only variable shear simulations, indicating the non-trivial impact of this higher-order blending effect. We also explored the impact of the PSF modelling errors and found a small yet noticeable effect on the shear bias. Finally, we conducted a series of sensitivity tests, including changing the input galaxy properties. We conclude that our fiducial shape measurement algorithm, lensfit, is robust within the requirements of lensing analyses with KiDS. As for future weak lensing surveys with tighter requirements, we suggest further investments in understanding the impact of blends at different redshifts, improving the PSF modelling algorithm and developing the shape measurement method to be less sensitive to the galaxy properties.Comment: 28 pages, 31 figures, 2 tables, minor revisions to match the final accepted versio

Abstract:

We consider the bias introduced by a spatially varying multiplicative shear bias (m-bias) on tomographic cosmic shear angular power spectra. To compute the bias in the power spectra, we estimate the mode-coupling matrix associated with an m-bias map using a computationally efficient pseudo-C_ℓ method. This allows us to consider the effect of the m-bias to high ℓ. We then conduct a Fisher matrix analysis to forecast resulting biases in cosmological parameters. For a Euclid-like survey with a spatially varying m-bias, with zero mean and rms of 0.01, we find that parameter biases reach a maximum of ∼10 per cent of the expected statistical error, if multipoles up to ℓ_max = 5000 are included. We conclude that the effect of the spatially varying m-bias may be a subdominant but potentially non-negligible contribution to the error budget in forthcoming weak lensing surveys. We also investigate the dependence of parameter biases on the amplitude and angular scale of spatial variations of the m-bias field, and conclude that requirements should be placed on the rms of spatial variations of the m-bias, in addition to any requirement on the mean value. We find that, for a Euclid-like survey, biases generally exceed ∼30 per cent of the statistical error for m-bias rms ∼0.02–0.03 and can exceed the statistical error for rms ∼0.04–0.05. This allows requirements to be set on the permissible amplitude of spatial variations of the m-bias that will arise due to systematics in forthcoming weak lensing measurements.