Constraining the astrophysics and cosmology from 21cm tomography using
  deep learning with the SKA
  Future Square Kilometre Array (SKA) surveys are expected to generate huge
datasets of 21cm maps on cosmological scales from the Epoch of Reionization
(EoR). We assess the viability of exploiting machine learning techniques,
namely, convolutional neural networks (CNN), to simultaneously estimate the
astrophysical and cosmological parameters from 21cm maps from semi-numerical
simulations. We further convert the simulated 21cm maps into SKA-like mock maps
using the detailed SKA antennae distribution, thermal noise and a recipe for
foreground cleaning. We successfully design two CNN architectures (VGGNet-like
and ResNet-like) that are both efficiently able to extract simultaneously three
astrophysical parameters, namely the photon escape fraction (f$_{\rm esc}$),
the ionizing emissivity power dependence on halo mass ($C_{\rm ion}$) and the
ionizing emissivity redshift evolution index ($D_{\rm ion}$), and three
cosmological parameters, namely the matter density parameter ($\Omega_{m}$),
the dimensionless Hubble constant ($h$), and the matter fluctuation amplitude
($\sigma_{8}$), from 21cm maps at several redshifts. With the presence of noise
from SKA, our designed CNNs are still able to recover these astrophysical and
cosmological parameters with great accuracy ($R^{2} > 92\%$), improving to
$R^{2} > 99\%$ towards low redshift and low neutral fraction values. Our
results show that future 21cm observations can play a key role to break
degeneracy between models and tightly constrain the astrophysical and
cosmological parameters, using only few frequency channels.
