Mocking Faint Black Holes during Reionization

TL;DR

This paper uses a neural network trained on galaxy properties to estimate the population of faint black holes during reionization, predicting their impact on hydrogen ionization and the 21 cm line observations.

Contribution

It introduces a neural network approach to estimate faint black hole populations during reionization, extending predictions to lower masses and assessing their role in hydrogen ionization.

Findings

Faint black holes could significantly contribute to reionization without conflicting with observations.

Black holes grow more efficiently at higher redshifts but follow a consistent galaxy-BH relation.

The study provides a power-law model for the hydrogen ionizing emissivity of black holes.

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 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.