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KiDS-Legacy calibration: unifying shear and redshift calibration with the SKiLLS multi-band image simulations

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arxiv 2210.07163 v2 pith:H2TMKCZ3 submitted 2022-10-13 astro-ph.CO

classification astro-ph.CO
keywords shearimpactsimulationscalibrationlensingpropertiesalgorithmanalysis
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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.

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  1. Probabilistic redshift estimation of unresolved galaxies from multi-band background light maps

    astro-ph.CO 2026-07 conditional novelty 6.0 of 10

    Conditional normalising flows trained on mock multi-band images recover the redshift distribution of unresolved galaxies with sub-percent accuracy in mean and width, under idealized simulation-matched conditions.

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