Inferring Astrophysics and Dark Matter Properties from 21cm Tomography
  using Deep Learning
  21cm tomography opens a window to directly study astrophysics and fundamental
physics of early epochs in our Universe's history, the Epoch of Reionisation
(EoR) and Cosmic Dawn (CD). Summary statistics such as the power spectrum omit
information encoded in this signal due to its highly non-Gaussian nature. Here
we adopt a network-based approach for direct inference of CD and EoR
astrophysics jointly with fundamental physics from 21cm tomography. We showcase
a warm dark matter (WDM) universe, where dark matter density parameter
$\Omega_\mathrm{m}$ and WDM mass $m_\mathrm{WDM}$ strongly influence both CD
and EoR. Reflecting the three-dimensional nature of 21cm light-cones, we
present a new, albeit simple, 3D convolutional neural network for efficient
parameter recovery at moderate training cost. On simulations we observe
high-fidelity parameter recovery for CD and EoR astrophysics ($R^2>0.78-0.99$),
together with DM density $\Omega_\mathrm{m}$ ($R^2>0.97$) and WDM mass
($R^2>0.61$, significantly better for $m_\mathrm{WDM}<3-4\,$keV). For realistic
mock observed light-cones that include noise and foreground levels expected for
the Square Kilometre Array, we note that in an optimistic foreground scenario
parameter recovery is unaffected, while for moderate, less optimistic
foreground levels (occupying the so-called wedge) the recovery of the WDM mass
deteriorates, while other parameters remain robust against increased foreground
levels at $R^2>0.9$. We further test the robustness of our network-based
inference against modelling uncertainties and systematics by transfer learning
between bare simulations and mock observations; we find robust recovery of
specific X-ray luminosity and ionising efficiency, while DM density and WDM
mass come with increased bias and scatter.
