Implicit Likelihood Inference of Reionization Parameters from the 21 cm
  Power Spectrum
  The first measurements of the 21 cm brightness temperature power spectrum
from the epoch of reionization will be very likely achieved in the near future
by radio interferometric array experiments such as the Hydrogen Epoch of
Reionization Array (HERA) and the precursors of the Square Kilometre Array
(SKA). Standard MCMC analyses use an explicit likelihood approximation to infer
the reionization and astrophysical parameters from the 21 cm power spectrum. In
this paper, we present a new Bayesian inference of the reionization parameters
where the likelihood is implicitly defined through forward simulations using
density estimation likelihood-free inference (DELFI). Realistic effects
including thermal noise and foreground avoidance are also applied to the mock
observations from the HERA and SKA. We demonstrate that this method recovers
accurate posterior distributions for the reionization parameters, and
outperforms the standard MCMC analysis in terms of the location and size of
credible parameter regions. With the minutes-level processing time once the
network is trained, this technique is a promising approach for the scientific
interpretation of future 21 cm power spectrum observation data. Our code
21cmDELFI-PS is publicly available at this link.
