Learning neutrino effects in Cosmology with Convolutional Neural Networks

• URL: http://arxiv.org/abs/1910.04255v2
• Date: Thu, 21 Sep 2023 19:10:44 GMT
• Title: Learning neutrino effects in Cosmology with Convolutional Neural Networks
• Authors: Elena Giusarma, Mauricio Reyes Hurtado, Francisco Villaescusa-Navarro, Siyu He, Shirley Ho, ChangHoon Hahn
• Abstract summary: We propose a new method to generate simulations with massive neutrinos from standard $Lambda$CDM simulations without neutrinos. Our method allows us to generate massive neutrino simulations 10,000 times faster than the traditional methods.
• Score: 1.1151500945305677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Measuring the sum of the three active neutrino masses, $M_\nu$, 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 -- several hundred to thousand core-hours for each neutrino mass case. In this work, we propose a new method, based on a deep learning network, to quickly generate simulations with massive neutrinos from standard $\Lambda$CDM simulations without neutrinos. We computed multiple relevant statistical measures of deep-learning generated simulations, and conclude that our approach is an accurate alternative to the traditional N-body techniques. In particular the power spectrum is within $\simeq 6\%$ down to non-linear scales $k=0.7$~\rm h/Mpc. Finally, our method allows us to generate massive neutrino simulations 10,000 times faster than the traditional methods.

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