Extracting cosmological parameters from N-body simulations using machine
  learning techniques
  We make use of snapshots taken from the Quijote suite of simulations,
consisting of 2000 simulations where five cosmological parameters have been
varied ($\Omega_m$, $\Omega_b$, $h$, $n_s$ and $\sigma_8$) in order to
investigate the possibility of determining them using machine learning
techniques. In particular, we show that convolutional neural networks can be
employed to accurately extract $\Omega_m$ and $\sigma_8$ from the N-body
simulations, and that these parameters can also be found from the non-linear
matter power spectrum obtained from the same suite of simulations using both
random forest regressors and deep neural networks. We show that the power
spectrum provides competitive results in terms of accuracy compared to using
the simulations and that we can also estimate the scalar spectral index $n_s$
from the power spectrum, at a lower precision.
