A simulation-based inference pipeline for cosmic shear with the Kilo-Degree Survey

TL;DR

This paper introduces a simulation-based inference pipeline for cosmic shear analysis that accurately recovers the full posterior distribution using fewer simulations than traditional methods, offering a flexible alternative to Gaussian likelihood approaches.

Contribution

The authors develop and demonstrate a simulation-based inference method for cosmic shear data that overcomes limitations of standard Gaussian likelihood models, achieving accurate posterior recovery with fewer simulations.

Findings

SBI recovers the full 12D KiDS posterior with under 10,000 simulations.

The method is robust to suboptimal choices of fiducial parameters and covariance.

SBI offers a more versatile alternative to standard inference methods.

Abstract

The standard approach to inference from cosmic large-scale structure data employs summary statistics that are compared to analytic models in a Gaussian likelihood with pre-computed covariance. To overcome the idealising assumptions about the form of the likelihood and the complexity of the data inherent to the standard approach, we investigate simulation-based inference (SBI), which learns the likelihood as a probability density parameterised by a neural network. We construct suites of simulated, exactly Gaussian-distributed data vectors for the most recent Kilo-Degree Survey (KiDS) weak gravitational lensing analysis and demonstrate that SBI recovers the full 12-dimensional KiDS posterior distribution with just under $10^4$ simulations. We optimise the simulation strategy by initially covering the parameter space by a hypercube, followed by batches of actively learnt additional points. The data compression in our SBI implementation is robust to suboptimal choices of fiducial parameter values and of data covariance. Together with a fast simulator, SBI is therefore a competitive and more versatile alternative to standard inference.