Super-Eddington accretion in protogalactic cores
Year: 2026
Type: article
Abstract: The presence of massive black holes (BHs) exceeding 10 9 M ⊙ already at redshift z > 6 challenges standard models of BH growth. Super-Eddington (SE) accretion has emerged as a promising mechanism to solve this issue, yet its impact on early BH evolution in tailored numerical experiments remains largely unexplored. In this work, we investigate the growth of BH seeds embedded in a gas-rich, metal-poor protogalaxy at z ∼ 15 using a suite of high-resolution hydrodynamical simulations that implement a slim-disc-based SE accretion model. We explored a broad parameter space, varying the initial BH mass, feedback efficiency, and spin. We find that SE accretion enables rapid growth in all cases, allowing BHs to accrete up to 10 5 M ⊙ within a few 10 3 –10 4 years, independent of seed properties. Feedback regulates this process, both by depleting central gas and altering BH dynamics via star formation-driven potential fluctuations, yet even the strongest feedback regimes permit significantly greater growth than the Eddington-limited case. Growth stalls after less than ∼1 Myr due to local gas exhaustion, as no large-scale inflows are present in the adopted numerical setup. Our results show that SE accretion naturally leads to BHs that are overmassive relative to their host galaxy stellar content, consistent with JWST observations. We conclude that short low-duty-cycle SE episodes represent a viable pathway for assembling the most massive BHs observed at early cosmic times, even when starting from light seeds.
Source: Astronomy and Astrophysics
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,865
Awards: G231133531, Gas-embedded black hole binaries and extreme mass ratio inspirals in the LISA discovery space; linking galactic environments to gravitational wave-driven in-spiral, G4895826563, G5074879826, GW-Learn: Deciphering the Gravitation-Wave Universe using the Next-Generation Observatories and Machine Learning, G972786147
OpenAlex ID: W4415987638
