Deep learning dark matter map reconstructions from DES SV weak lensing data. Issue 4 (16th January 2020)

• Record Type: Journal Article
• Authors: Jeffrey, Niall; Lanusse, François; Lahav, Ofer; Starck, Jean-Luc
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 492:Issue 4(2020)
• Page Start: 5023
• Page End: 5029
• Publication Date: 2020-01-16
• Subjects: gravitational lensing: weak -- methods: statistical -- (cosmology:) large-scale structure of Universe; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/staa127
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: We present the first reconstruction of dark matter maps from weak lensing observational data using deep learning. We train a convolution neural network with a U-Net-based architecture on over 3.6 × 10 5 simulated data realizations with non-Gaussian shape noise and with cosmological parameters varying over a broad prior distribution. We interpret our newly created dark energy survey science verification (DES SV) map as an approximation of the posterior mean P (κ| γ ) of the convergence given observed shear. Our DeepMass 1 method is substantially more accurate than existing mass-mapping methods. With a validation set of 8000 simulated DES SV data realizations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean square error (MSE) by 11 per cent. With N -body simulated MICE mock data, we show that Wiener filtering, with the optimal known power spectrum, still gives a worse MSE than our generalized method with no input cosmological parameters; we show that the improvement is driven by the non-linear structures in the convergence. With higher galaxy density in future weak lensing data unveiling more non-linear scales, it is likely that deep learning will be a leading approach for mass mapping with Euclid and LSST.