  Traditional estimators of the galaxy power spectrum and bispectrum are
sensitive to the survey geometry. They yield spectra that differ from the true
underlying signal since they are convolved with the window function of the
survey. For the current and future generations of experiments, this bias is
statistically significant on large scales. It is thus imperative that the
effect of the window function on the summary statistics of the galaxy
distribution is accurately modelled. Moreover, this operation must be
computationally efficient in order to allow sampling posterior probabilities
while performing Bayesian estimation of the cosmological parameters. In order
to satisfy these requirements, we built a deep neural network model that
emulates the convolution with the window function, and we show that it provides
fast and accurate predictions. We trained (tested) the network using a suite of
2000 (200) cosmological models within the cold dark matter scenario, and
demonstrate that its performance is agnostic to the precise values of the
cosmological parameters. In all cases, the deep neural network provides models
for the power spectra and the bispectrum that are accurate to better than 0.1
per cent on a timescale of 10 $\mu$s.
