Cosmic evolution of the incidence of active galactic nuclei in massive clusters: simulations versus observations
Bibliographic record
Abstract
ABSTRACT This paper explores the role of small-scale environment (<1 Mpc) in modulating accretion events on to supermassive black holes by studying the incidence of active galactic nuclei (AGNs) in massive clusters of galaxies. A flexible, data-driven semi-empirical model is developed based on a minimal set of parameters and under the zero-order assumption that the incidence of AGNs in galaxies is independent of environment. This is used to predict how the fraction of X-ray selected AGN among galaxies in massive dark matter haloes ($\gtrsim 3\times 10^{14}\, \mathrm{M}_{\odot }$) evolves with redshift and reveal tensions with observations. At high redshift, z ∼ 1.2, the model underpredicts AGN fractions, particularly at high X-ray luminosities, $L_X(\rm 2\rm{-}10\, keV) \gtrsim 10^{44}\, erg \, s^{-1}$. At low redshift, z ∼ 0.2, the model estimates fractions of moderate luminosity AGN ($L_X(\rm 2\rm{-}10\, keV) \gtrsim 10^{43}\, erg \, s^{-1}$) that are a factor of 2–3 higher than the observations. These findings reject the zero-order assumption on which the semi-empirical model hinges and point to a strong and redshift-dependent influence of the small-scale environment on the growth of black holes. Cluster of galaxies appear to promote AGN activity relative to the model expectation at z ∼ 1.2 and suppress it close to the present day. These trends could be explained by the increasing gas content of galaxies towards higher redshift combined with an efficient triggering of AGNs at earlier times in galaxies that fall on to clusters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".