PkANN - II. A non-linear matter power spectrum interpolator developed
  using artificial neural networks
  In this paper we introduce PkANN, a freely available software package for
interpolating the non-linear matter power spectrum, constructed using
Artificial Neural Networks (ANNs). Previously, using Halofit to calculate
matter power spectrum, we demonstrated that ANNs can make extremely quick and
accurate predictions of the power spectrum. Now, using a suite of 6380 N-body
simulations spanning 580 cosmologies, we train ANNs to predict the power
spectrum over the cosmological parameter space spanning $3\sigma$ confidence
level (CL) around the concordance cosmology. When presented with a set of
cosmological parameters ($\Omega_{\rm m} h^2, \Omega_{\rm b} h^2, n_s, w,
\sigma_8, \sum m_\nu$ and redshift $z$), the trained ANN interpolates the power
spectrum for $z\leq2$ at sub-per cent accuracy for modes up to
$k\leq0.9\,h\textrm{Mpc}^{-1}$. PkANN is faster than computationally expensive
N-body simulations, yet provides a worst-case error $<1$ per cent fit to the
non-linear matter power spectrum deduced through N-body simulations. The
overall precision of PkANN is set by the accuracy of our N-body simulations, at
5 per cent level for cosmological models with $\sum m_\nu<0.5$ eV for all
redshifts $z\leq2$. For models with $\sum m_\nu>0.5$ eV, predictions are
expected to be at 5 (10) per cent level for redshifts $z>1$ ($z\leq1$). The
PkANN interpolator may be freely downloaded from
http://zuserver2.star.ucl.ac.uk/~fba/PkANN
