Simulation-Based Inference of Reionization Parameters From 3D
  Tomographic 21 cm Lightcone Images
  Tomographic three-dimensional 21 cm images from the epoch of reionization
contain a wealth of information about the reionization of the intergalactic
medium by astrophysical sources. Conventional power spectrum analysis cannot
exploit the full information in the 21 cm data because the 21 cm signal is
highly non-Gaussian due to reionization patchiness. We perform a Bayesian
inference of the reionization parameters where the likelihood is implicitly
defined through forward simulations using density estimation likelihood-free
inference (DELFI). We adopt a trained 3D Convolutional Neural Network (CNN) to
compress the 3D image data into informative summaries (DELFI-3D CNN). We show
that this method recovers accurate posterior distributions for the reionization
parameters. Our approach outperforms earlier analysis based on two-dimensional
21 cm images. In contrast, an MCMC analysis of the 3D lightcone-based 21 cm
power spectrum alone and using a standard explicit likelihood approximation
results in less accurate credible parameter regions than inferred by the
DELFI-3D CNN, both in terms of the location and shape of the contours. Our
proof-of-concept study implies that the DELFI-3D CNN can effectively exploit
more information in the 3D 21 cm images than a 2D CNN or power spectrum
analysis. This technique can be readily extended to include realistic effects
and is therefore a promising approach for the scientific interpretation of
future 21 cm observation data.
