Probing Ultra-light Axion Dark Matter from 21cm Tomography using
  Convolutional Neural Networks
  We present forecasts on the detectability of Ultra-light axion-like particles
(ULAP) from future 21cm radio observations around the epoch of reionization
(EoR). We show that the axion as the dominant dark matter component has a
significant impact on the reionization history due to the suppression of small
scale density perturbations in the early universe. This behavior depends
strongly on the mass of the axion particle.
  Using numerical simulations of the brightness temperature field of neutral
hydrogen over a large redshift range, we construct a suite of training data.
This data is used to train a convolutional neural network that can build a
connection between the spatial structures of the brightness temperature field
and the input axion mass directly. We construct mock observations of the future
Square Kilometer Array survey, SKA1-Low, and find that even in the presence of
realistic noise and resolution constraints, the network is still able to
predict the input axion mass. We find that the axion mass can be recovered over
a wide mass range with a precision of approximately 20\%, and as the whole DM
contribution, the axion can be detected using SKA1-Low at 68\% if the axion
mass is $M_X<1.86 \times10^{-20}$eV although this can decrease to $M_X<5.25
\times10^{-21}$eV if we relax our assumptions on the astrophysical modeling by
treating those astrophysical parameters as nuisance parameters.
