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arXiv · 2407.09949

The formation of supermassive black holes from Population III.1 seeds. III. Galaxy evolution and black hole growth from semi-analytic modelling

Abstract

We present an implementation of Pop III.1 seeding of supermassive black holes (SMBHs) in a theoretical model of galaxy formation and evolution to assess the growth the SMBH population and the properties of the host galaxies. The model of Pop III.1 seeding involves SMBH formation at redshifts $z\gtrsim 20$ in dark matter minihalos that are isolated from external radiative feedback, parameterized by isolation distance $d_{\rm iso}$. Within a standard $Λ$CDM cosmology, we generate dark matter halos using the code PINOCCHIO and seed them according to the Pop III.1 scenario, exploring values of $d_{\rm iso}$ from 50 to 100~kpc (proper distance). We consider two alternative cases of SMBH seeding: a Halo Mass Threshold (HMT) model in which all halos $>7\times10^{10}\:M_\odot$ are seeded with $\sim 10^5\:M_\odot$ black holes; an All Light Seed (ALS) model in which all halos are seeded with low, stellar-mass black holes. We follow the redshift evolution of the halos, populating them with galaxies using the GAlaxy Evolution and Assembly theoretical model of galaxy formation, including accretion on SMBHs and related feedback processes. Here we present predictions for the properties of galaxy populations, focusing on stellar masses, star formation rates, and black hole masses. The local, $z\sim0$ metrics of occupation fraction as a function of the galaxy stellar mass, galaxy stellar mass function (GSMF), and black hole mass function (BHMF) all suggest a constraint of $d_{\rm iso}<75\:$kpc. We discuss the implications of this result for the Pop III.1 seeding mechanism.

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BibTeXRIS

Vieri Cammelli, Pierluigi Monaco, Jonathan C. Tan, Jasbir Singh, Fabio Fontanot, Gabriella De Lucia, Michaela Hirschmann, Lizhi Xie. 2025-07-18. The formation of supermassive black holes from Population III.1 seeds. III. Galaxy evolution and black hole growth from semi-analytic modelling. https://arxiv.org/abs/2407.09949

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