Learning neutrino effects in Cosmology with Convolutional Neural Networks
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Measuring the sum of the three active neutrino masses, M_ν, is one of the most important challenges in modern cosmology. Massive neutrinos imprint characteristic signatures on several cosmological observables in particular on the large-scale structure of the Universe. In order to maximize the information that can be retrieved from galaxy surveys, accurate theoretical predictions in the non-linear regime are needed. Currently, one way to achieve those predictions is by running cosmological numerical simulations. Unfortunately, producing those simulations requires high computational resources – seven hundred CPU hours for each neutrino mass case. In this work, we propose a new method, based on a deep learning network (U-Net), to quickly generate simulations with massive neutrinos from standard ΛCDM simulations without neutrinos. We computed multiple relevant statistical measures of deep-learning generated simulations, and conclude that our method accurately reproduces the 3-dimensional spatial distribution of matter down to non-linear scales: k < 0.7 h/Mpc. Finally, our method allows us to generate massive neutrino simulations 10,000 times faster than the traditional methods.
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