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The first measurements of the 21 cm brightness temperature power spectrumfrom the epoch of reionization will very likely be achieved in the near futureby radio interferometric array experiments such as the Hydrogen Epoch ofReionization Array (HERA) and the Square Kilometre Array (SKA). Standard MCMCanalyses use an explicit likelihood approximation to infer the reionizationparameters from the 21 cm power spectrum. In this paper, we present a newBayesian inference of the reionization parameters where the likelihood isimplicitly defined through forward simulations using density estimationlikelihood-free inference (DELFI). Realistic effects including thermal noiseand foreground avoidance are also applied to the mock observations from theHERA and SKA. We demonstrate that this method recovers accurate posteriordistributions for the reionization parameters, and outperforms the standardMCMC analysis in terms of the location and size of credible parameter regions.With the minutes-level processing time once the network is trained, thistechnique is a promising approach for the scientific interpretation of future21 cm power spectrum observation data. Our code 21cmDELFI-PS is publiclyavailable at this link.

Implicit Likelihood Inference of Reionization Parameters from the 21 cm Power Spectrum