High-redshift AGN population in radiation-hydrodynamics simulations
Bibliographic record
Abstract
ABSTRACT High-redshift active galactic nuclei (AGNs) have long been recognized as key probes of early black hole growth and galaxy evolution. However, modelling this population remains difficult due to the wide range of luminosities and black hole masses involved, and the high computational costs of capturing the hydrodynamic response of gas and evolving radiation fields on the fly. In this study, we present a new suite of simulations based on the IllustrisTNG galaxy formation framework, enhanced with on-the-fly radiative transfer, to examine AGN at high redshift ($z \gtrsim 5$) in a protocluster environment extracted from the MillenniumTNG simulation. We focus on the coevolution of black holes and their host galaxies, as well as the radiative impact on surrounding intergalactic gas. The model predicts that black holes form in overdense regions and lie below the local black hole–stellar mass relation, with stellar mass assembly preceding significant black hole accretion. Ionizing photons are primarily produced by stars, which shape the morphology of ionized regions and drive reionization. Given the restrictive black hole growth in the original IllustrisTNG model, we reduce the radiative efficiency from 0.2 to 0.1, resulting in higher accretion rates for massive black holes, more bursty growth, and earlier AGN-driven quenching. However, the resulting AGNs remain significantly fainter than observed high-redshift quasars. As such, to incorporate this missing population, we introduce a quasar boosted model, in which we artificially boost the AGN luminosity. This results in strong effects on the surrounding gas, most notably a proximity effect, and large contributions to He ionization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".