Impact of PSF misestimation and galaxy population bias on precision shear measurement using a CNN

Weak gravitational lensing of distant galaxies provides a powerful probe of dark energy. The aim of this study is to investigate the application of convolutional neural networks (CNNs) to precision shear estimation. In particular, using a shallow CNN, we explore the impact of point spread function (PSF) misestimation and `galaxy population bias' (including `distribution bias' and `morphology bias'), focusing on the accuracy requirements of next generation surveys. We simulate a population of noisy disk and elliptical galaxies and adopt a …

aim application astro-ph.co bias cnn cnns convolutional neural networks cs.lg dark energy energy explore galaxy impact measurement networks neural networks population precision probe psf shear study