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 in the cosmological hydrodynamical simulation Massive-Black II. We then populate all galaxies in the simulation from to 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 ionized at 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 , but mainly follow a redshift-independent galaxy-BH relation. We provide a power law parametrisation of the hydrogen ionizing emissivity of BHs.
Keywords
Cite
@article{arxiv.2010.09722,
title = {Mocking Faint Black Holes during Reionization},
author = {Marius B. Eide and Benedetta Ciardi and Yu Feng and Tiziana Di Matteo},
journal= {arXiv preprint arXiv:2010.09722},
year = {2020}
}
Comments
8 pages, 7 figures, accepted for publication in MNRAS