Differentiable Forward Modeling for Efficient and Accurate Shear Inference

Differentiable Forward Modeling for Efficient and Accurate Shear Inference
Ismael Mendoza, Axel Guinot, Matthew R. Becker, Camille Avestruz, Jean-Eric Campagne, Natalia Porqueres, Michael Schneider, Eleni Tsaprazi, the LSST Dark Energy Science Collaboration
arXiv:2604.22048v2 Announce Type: replace
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 $0.9 times 10^{-3} , [3sigma]$ when the intrinsic distribution of galaxy properties is known, and below $1.3 times 10^{-3}, [3sigma]$ when these distributions are inferred alongside shear. Both results are within the LSST requirement of $|m| arXiv:2604.22048v2 Announce Type: replace
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 $0.9 times 10^{-3} , [3sigma]$ when the intrinsic distribution of galaxy properties is known, and below $1.3 times 10^{-3}, [3sigma]$ when these distributions are inferred alongside shear. Both results are within the LSST requirement of $|m|