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We present the first reconstruction of dark matter maps from weak lensingobservational data using deep learning. We train a convolution neural network(CNN) with a Unet based architecture on over $3.6\times10^5$ simulated datarealizations with non-Gaussian shape noise and with cosmological parametersvarying over a broad prior distribution. We interpret our newly created DES SVmap as an approximation of the posterior mean $P(\kappa | \gamma)$ of theconvergence given observed shear. Our DeepMass method is substantially moreaccurate than existing mass-mapping methods. With a validation set of 8000simulated DES SV data realizations, compared to Wiener filtering with a fixedpower spectrum, the DeepMass method improved the mean-square-error (MSE) by 11per cent. With N-body simulated MICE mock data, we show that Wiener filteringwith the optimal known power spectrum still gives a worse MSE than ourgeneralized method with no input cosmological parameters; we show that theimprovement is driven by the non-linear structures in the convergence. Withhigher galaxy density in future weak lensing data unveiling more non-linearscales, it is likely that deep learning will be a leading approach for massmapping with Euclid and LSST.

Deep learning dark matter map reconstructions from DES SV weak lensing data