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We develop a fully non-invasive use of machine learning in order to enableopen research on Euclid-sized data sets. Our algorithm leaves complete controlover theory and data analysis, unlike many black-box like uses of machinelearning. Focusing on a `3x2 analysis' which combines cosmic shear, galaxyclustering and tangential shear at a Euclid-like sky coverage, we arrange atotal of 348000 data points into data matrices whose structure permits not onlyan easy prediction by neural nets, but it additionally permits the essentialremoval from the data of patterns which the neural nets could not `understand'.The latter provides an often lacking mechanism to control and debias theinference of physics. The theoretical backbone to our neural net training canbe any conventional (deterministic) theory code, where we chose CLASS. Aftertraining, we infer the seven parameters of a $w$CDM cosmology by Monte CarloMarkov sampling posteriors at Euclid-like precision within a day. We publiclyprovide the neural nets which memorise and output all 3x2 power spectra at aEuclid-like sky coverage and redshift binning.

Euclid-era cosmology for everyone: Neural net assisted MCMC sampling for the joint 3x2 likelihood