Authors: | A. Peel, F. Lalande, J.-L. Starck, V. Pettorino, J. Merten, C. Giocoli, M. Meneghetti, M. Baldi |

Journal: | Submitted to PRL |

Year: | 2018 |

Download: | ADS | arXiv |

## Abstract

We present a convolutional neural network to identify distinct cosmological scenarios based on the weak-lensing maps they produce. Modified gravity models with massive neutrinos can mimic the standard concordance model in terms of Gaussian weak-lensing observables, limiting a deeper understanding of what causes cosmic acceleration. We demonstrate that a network trained on simulated clean convergence maps, condensed into a novel representation, can discriminate between such degenerate models with 83%-100% accuracy. Our method outperforms conventional statistics by up to 40% and is more robust to noise.