Mocking faint black holes during reionization

Abstract

To investigate the potential abundance and impact of nuclear black holes (BHs) during reionization, we generate a neural network that estimates their masses and accretion rates by training it on 23 properties of galaxies harbouring them at z = 6 in the cosmological hydrodynamical simulation MassiveBlack-II. We then populate all galaxies in the simulation from z = 18 to 5 with BHs from this network. As the network allows to robustly extrapolate to BH masses below those of the BH seeds, we predict a population of faint BHs with a turnover-free luminosity function, while retaining the bright (and observed) BHs, and together they predict a Universe in which intergalactic hydrogen is 15 per cent ionized at z = 6 for a clumping factor of 5. Faint BHs may play a stronger role in H reionization without violating any observational constraints. This is expected to have an impact also on pre-heating and pre-ionization, which is relevant to observations of the 21-cm line from neutral H. We also find that BHs grow more efficiently at higher z, but mainly follow a redshift-independent galaxy-BH relation. We provide a power law parametrization of the hydrogen ionizing emissivity of BHs.