Emulation of reionization simulations for Bayesian inference of
  astrophysics parameters using neural networks
  Next generation radio experiments such as LOFAR, HERA and SKA are expected to
probe the Epoch of Reionization and claim a first direct detection of the
cosmic 21cm signal within the next decade. Data volumes will be enormous and
can thus potentially revolutionize our understanding of the early Universe and
galaxy formation. However, numerical modelling of the Epoch of Reionization can
be prohibitively expensive for Bayesian parameter inference and how to
optimally extract information from incoming data is currently unclear.
Emulation techniques for fast model evaluations have recently been proposed as
a way to bypass costly simulations. We consider the use of artificial neural
networks as a blind emulation technique. We study the impact of training
duration and training set size on the quality of the network prediction and the
resulting best fit values of a parameter search. A direct comparison is drawn
between our emulation technique and an equivalent analysis using 21CMMC. We
find good predictive capabilities of our network using training sets of as low
as 100 model evaluations, which is within the capabilities of fully numerical
radiative transfer codes.
