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The standard approach to inference from cosmic large-scale structure dataemploys summary statistics that are compared to analytic models in a Gaussianlikelihood with pre-computed covariance. To overcome the idealising assumptionsabout the form of the likelihood and the complexity of the data inherent to thestandard approach, we investigate simulation-based inference (SBI), whichlearns the likelihood as a probability density parameterised by a neuralnetwork. We construct suites of simulated, exactly Gaussian-distributed datavectors for the most recent Kilo-Degree Survey (KiDS) weak gravitationallensing analysis and demonstrate that SBI recovers the full 12-dimensional KiDSposterior distribution with just under $10^4$ simulations. We optimise thesimulation strategy by initially covering the parameter space by a hypercube,followed by batches of actively learnt additional points. The data compressionin our SBI implementation is robust to suboptimal choices of fiducial parametervalues and of data covariance. Together with a fast simulator, SBI is thereforea competitive and more versatile alternative to standard inference.

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