Simulating cosmic reionization at large scales - II. The 21-cm emission features and statistical signals

Abstract

We present detailed predictions for the redshifted 21-cm signal from the epoch of reion-ization. These predictions are obtained from radiative transfer calculations on the results of large-scale (100 h-1 Mpc), high dynamic range, cosmological simulations. We consider several scenarios for the reionization history, of both early and extended reionizations. From the simulations, we construct and analyse a range of observational characteristics, from the global signal, via detailed images and spectra, to statistical representations of rms fluctuations, angular power spectra, and probability distribution functions to characterize the non-Gaussianity of the 21-cm signal. We find that the different reionization scenarios produce quite similar observational signatures, mostly differing in the redshifts of 50 per cent reionization, and of final overlap. All scenarios show a gradual transition in the global signatures of mean signal and rms fluctuations, which would make these more difficult to observe. Individual features, such as deep gaps and bright peaks, are substantially different from the mean, and mapping these with several arcminutes and 100 s of kHz resolution would provide a direct measurement of the underlying density field and the geometry of the cosmological H II regions, although significantly modified by peculiar velocity distortions. The presence of late emission peaks suggests these to be a useful target for observations. The power spectra during reionization are strongly boosted compared to the underlying density fluctuations. The strongest statistical signal is found around the time of 50 per cent reionization and displays a clear maximum at an angular scale of ℓ ∼ 3000-5000. We find the distribution function of emission features to be strongly non-Gaussian, with an order of magnitude higher probability of bright emission features. These results suggest that, observationally, it may be easier to find individual bright features than deriving the power spectra, which, in turn, is easier than observing individual images. © 2006 RAS.