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

## TL;DR

This paper introduces a deep learning approach using CNNs to reconstruct dark matter maps from weak lensing data, outperforming traditional methods in accuracy and handling non-linear structures.

## Contribution

The authors develop DeepMass, a CNN-based method that improves dark matter map reconstruction accuracy over existing techniques using simulated DES SV data.

## Key findings

- DeepMass reduces mean-square-error by 11% compared to Wiener filtering.
- It outperforms traditional methods even without prior cosmological parameters.
- Deep learning effectively captures non-linear structures in convergence maps.

## Abstract

We present the first reconstruction of dark matter maps from weak lensing observational data using deep learning. We train a convolution neural network (CNN) with a Unet based architecture on over $3.6\times10^5$ simulated data realizations with non-Gaussian shape noise and with cosmological parameters varying over a broad prior distribution. We interpret our newly created DES SV map as an approximation of the posterior mean $P(\kappa | \gamma)$ of the convergence given observed shear. Our DeepMass 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.