Differentiable Forward Modeling for Efficient and Accurate Shear Inference

TL;DR

This paper introduces a differentiable Bayesian shear inference framework that accurately estimates cosmic shear from simulated galaxy images, achieving low bias and high computational efficiency suitable for large-scale surveys.

Contribution

It presents a novel implementation of shear inference that propagates pixel noise errors automatically and leverages differentiable models and GPUs for fast, accurate results.

Findings

Bias below LSST requirement of 2×10^{-3} in simulations

Achieves 300 effective MCMC samples in 0.45 seconds per galaxy on GPU

Progress towards handling real survey complexities like selection and shear biases

Abstract

Forthcoming Stage-IV dark energy optical surveys, such as LSST, have the ambitious goal of measuring cosmological parameters at sub-percent precision. Realizing their full scientific potential requires very precise measurement of the cosmic shear signal and control of corresponding systematics. In this work, we present a modern implementation of the Bayesian shear inference framework in Schneider et al. (2014), in the case that the PSF and sky background are known. This framework automatically propagates the pixel-noise measurement error from each galaxy into the final shear estimate, and thus requires no external calibration to handle noise bias. As a first application of this new implementation, we infer the cosmic shear posterior from simulated images consisting of isolated exponential galaxies with LSST-like levels of shape and pixel noise. In this simplified scenario, we estimate the absolute multiplicative bias $|m|$ of our approach to be below $0.9 \times 10^{-3} \, [3\sigma]$ when the intrinsic distribution of galaxy properties is known, and below $1.3 \times 10^{-3}\, [3\sigma]$ when these distributions are inferred alongside shear. Both results are within the LSST requirement of $|m| < 2 \times10^{-3}$. Additionally, we make progress towards the algorithm's computational feasibility in the context of modern wide-field surveys, where billions of galaxies must be processed, by leveraging differentiable forward models of galaxies, gradient-based samplers, and GPUs. Our final galaxy-fitting MCMC produces $300$ effective samples of galaxy properties in $0.45$ seconds per galaxy using a single A100 GPU. In the future, we seek to generalize our algorithm to handle selection, detection, and model shear biases so it can be applied to real survey data.