Mocking Faint Black Holes during Reionization
  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
Massive-Black II. We then populate all galaxies in the simulation from $z=18$
to $z=5$ 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 $15\%$ 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 -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 parametrisation of the hydrogen ionizing emissivity of BHs.
