# Learning neutrino effects in Cosmology with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1910.04255 · HTML: https://mlsanity.com/paper/1910.04255/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter](https://mlsanity.com/paper/2201.01305/) (2022)
- [COmoving Computer Acceleration (COCA): N-body simulations in an emulated frame of reference](https://mlsanity.com/paper/2409.02154/) (2024)
- [Super-resolving Dark Matter Halos using Generative Deep Learning](https://mlsanity.com/paper/2111.06393/) (2021)

## Abstract (arXiv)

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 LambdaCDM 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.