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In many cosmological inference problems, the likelihood (the probability ofthe observed data as a function of the unknown parameters) is unknown orintractable. This necessitates approximations and assumptions, which can leadto incorrect inference of cosmological parameters, including the nature of darkmatter and dark energy, or create artificial model tensions. Likelihood-freeinference covers a novel family of methods to rigorously estimate posteriordistributions of parameters using forward modelling of mock data. We presentlikelihood-free cosmological parameter inference using weak lensing maps fromthe Dark Energy Survey (DES) SV data, using neural data compression of weaklensing map summary statistics. We explore combinations of the power spectra,peak counts, and neural compressed summaries of the lensing mass map using deepconvolution neural networks. We demonstrate methods to validate the inferenceprocess, for both the data modelling and the probability density estimationsteps. Likelihood-free inference provides a robust and scalable alternative forrigorous large-scale cosmological inference with galaxy survey data (for DES,Euclid and LSST). We have made our simulated lensing maps publicly available.

Likelihood-free inference with neural compression of DES SV weak lensing map statistics