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 PSF that is representative of a Euclid-like survey. We quantify the accuracy achieved by the CNN assuming a linear relationship between the estimated and true shears and measure the multiplicative ($m$) and additive ($c$) biases. We make use of an unconventional loss function to mitigate the effects of noise bias and measure $m$ and $c$ when we use either: (i) an incorrect galaxy ellipticity distribution or size-magnitude relation, or the wrong ratio of morphological types, to describe the population of galaxies (distribution bias); (ii) an incorrect galaxy light profile (morphology bias); or (iii) a PSF with size or ellipticity offset from its true value (PSF misestimation). We compare our results to the Euclid requirements on the knowledge of the PSF model shape and size. Finally, we outline further work to build on the promising potential of CNNs in precision shear estimation.