Deep learning from 21-cm tomography of the Cosmic Dawn and Reionization
  The 21-cm power spectrum (PS) has been shown to be a powerful discriminant of
reionization and cosmic dawn astrophysical parameters. However, the 21-cm
tomographic signal is highly non-Gaussian. Therefore there is additional
information which is wasted if only the PS is used for parameter recovery. Here
we showcase astrophysical parameter recovery directly from 21-cm images, using
deep learning with convolutional neural networks (CNN). Using a database of 2D
images taken from 10,000 21-cm lightcones (each generated from different
cosmological initial conditions), we show that a CNN is able to recover
parameters describing the first galaxies: (i) Tvir , their minimum host halo
virial temperatures (or masses) capable of hosting efficient star formation;
(ii) {\zeta} , their typical ionizing efficiencies; (iii) LX/SFR , their
typical soft-band X-ray luminosity to star formation rate; and (iv) E0 , the
minimum X-ray energy capable of escaping the galaxy into the IGM. For most of
their allowed ranges, log Tvir and log LX/SFR are recovered with < 1%
uncertainty, while {\zeta} and E0 are recovered within 10% uncertainty. Our
results are roughly comparable to the accuracy obtained from Monte Carlo Markov
Chain sampling of the PS with 21CMMC for the two mock observations analyzed
previously, although we caution that we do not yet include noise and foreground
contaminants in this proof-of-concept study.
