Abstract The bubble size distribution of ionized hydrogen regions probes information about the morphology of H II bubbles during reionization. Conventionally, the H II bubble size distribution can be derived from the tomographic imaging data of the redshifted 21 cm signal from the epoch of reionization, which, however, is observationally challenging even for upcoming large radio interferometer arrays. Given that these interferometers promise to measure the 21 cm power spectrum accurately, we propose a new method, which is based on artificial neural networks, to reconstruct the H II bubble size distribution from the 21 cm power spectrum. We demonstrate that reconstruction from the 21 cm power spectrum can be almost as accurate as being directly measured from the imaging data with fractional error \(\lesssim\)10%, even with thermal noise at the sensitivity level of the Square Kilometre Array. Nevertheless, the reconstruction implicitly exploits the modeling in reionization simulations, and hence the recovered H II bubble size distribution is not an independent summary statistic from the power spectrum, and should be used only as an indicator for understanding H II bubble morphology and its evolution.
Keywords methods: data analysis – methods: numerical – (cosmology:) dark ages – reionization – first stars – (cosmology:) diffuse radiation – cosmology: theory
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