Speaker
Description
For weak lensing cosmological surveys, accurate measurement of galaxy shapes is of paramount importance. This requires image simulations that match the real data as closely as possible. So far, these simulations rely on parametric surface brightness profiles, but do not account for complex morphologies and substructure. In the era of Euclid, this can however lead to biases, given the unprecedented combination of depth and resolution of this mission.
We present a novel deep generative model based on a custom latent space compression via the wavelet scattering transform, trained on HST CANDELS data. This architecture allows conditional sampling over a multitude of galaxy properties such as ellipticity, magnitude and redshift, and is able to generate noise-free, PSF-independent galaxy images with complex galaxy morphologies across two HST filter bands covering the wavelength range of the Euclid VIS filter. This additionally enables simulation of color gradients.
Using this model, we create matching sets of large-scale Euclid-like image simulations branches with and without complex morphologies and color gradients. Afterwards, we compare the multiplicative biases of the shape measurement between these simulation branches and show that the inclusion of such a generative model into the image simulation pipelines of next generation weak lensing surveys is unavoidable in order to achieve sufficient shape measurement precision.