Neural Posterior Estimation for Inferring Weak Lensing Shear and Convergence from Pixels

Music City Center 

Inferring the distortion of imaged galaxies due to weak gravitational lensing is a challenging inverse problem involving pixelization, instrument bias, and a low signal-to-noise ratio. Most traditional approaches to this task produce point estimates of weak lensing shear and convergence by measuring, averaging, and calibrating galaxy ellipticities, a multistage procedure that is subject to image noise, selection bias, and model misspecification. As an alternative, we propose a Bayesian method for weak lensing inference that jointly estimates shear and convergence maps from multiband images using a type of likelihood-free amortized variational inference called neural posterior estimation (NPE). NPE is computationally efficient due to its utilization of deep neural networks and implicit marginalization of nuisance latent variables, and it provides estimates of posterior uncertainty that can be propagated to downstream cosmological analyses. When evaluated on synthetic images from the LSST-DESC DC2 Simulated Sky Survey, the proposed algorithm produces posterior shear and convergence maps that are well-calibrated and consistent with the ground truth.

neural posterior estimation

weak gravitational lensing

likelihood-free inference

variational inference

cosmology

astronomical images 

Section on Physical and Engineering Sciences