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

TL;DR

This paper introduces a likelihood-free inference method using neural compression of weak lensing map statistics to improve cosmological parameter estimation from DES SV data, addressing intractable likelihood issues.

Contribution

It presents a novel likelihood-free inference approach with neural data compression for weak lensing maps, validated for cosmological parameter estimation.

Findings

Successful application to DES SV data

Robust validation of inference process

Scalable method for large-scale surveys

Abstract

In many cosmological inference problems, the likelihood (the probability of the observed data as a function of the unknown parameters) is unknown or intractable. This necessitates approximations and assumptions, which can lead to incorrect inference of cosmological parameters, including the nature of dark matter and dark energy, or create artificial model tensions. Likelihood-free inference covers a novel family of methods to rigorously estimate posterior distributions of parameters using forward modelling of mock data. We present likelihood-free cosmological parameter inference using weak lensing maps from the Dark Energy Survey (DES) SV data, using neural data compression of weak lensing map summary statistics. We explore combinations of the power spectra, peak counts, and neural compressed summaries of the lensing mass map using deep convolution neural networks. We demonstrate methods to validate the inference process, for both the data modelling and the probability density estimation steps. Likelihood-free inference provides a robust and scalable alternative for rigorous large-scale cosmological inference with galaxy survey data (for DES, Euclid and LSST). We have made our simulated lensing maps publicly available.