Distinguishing standard and modified gravity cosmologies with machine learning

25 Oct 2018  ·  Austin Peel, Florian Lalande, Jean-Luc Starck, Valeria Pettorino, Julian Merten, Carlo Giocoli, Massimo Meneghetti, Marco Baldi ·

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.

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Cosmology and Nongalactic Astrophysics