Euclid-era cosmology for everyone: neural net assisted MCMC sampling for the joint 3 × 2 likelihood

We develop a fully non-invasive use of machine learning in order to enable open research on Euclid-sized data sets. Our algorithm leaves complete control over theory and data analysis, unlike many black-box-like uses of machine learning. Focusing on a '3 × 2 analysis' which combines cosmic shear, galaxy clustering, and tangential shear at a Euclid-like sky coverage, we arrange a total of 348 000 data points into data matrices whose structure permits not only an easy prediction by neural nets, but it also permits the essential removal from the data of patterns which the neural nets could not 'understand'. The latter provides an often lacking mechanism to control and debias the inference of physics. The theoretical backbone to our neural net training can be any conventional (deterministic) theory code, where we chose CLASS. After training, we infer the seven parameters of a wCDM cosmology by Monte Carlo Markov sampling posteriors at Euclid-like precision within a day. We publicly provide the neural nets which memorize and output all 3 × 2 power spectra at a Euclid-like sky coverage and redshift binning.