The small scatter in BH-host correlations and the case for self-regulated BH growth
Bibliographic record
Abstract
Supermassive black holes (BHs) obey tight scaling relations between their mass and host galaxy properties such as total stellar mass, velocity dispersion and potential well depth. This has led to the development of self-regulated models for BH growth, in which feedback from the central BH halts its own growth upon reaching a critical threshold. However, models have also been proposed in which feedback plays no role: so long as a fixed fraction of the host gas supply is accreted, relations like those observed can be reproduced. Here, we argue that the scatter in the observed BH–host correlations presents a demanding constraint on any model for these correlations, and that it favours self-regulated models of BH growth. We show that the scatter in the stellar mass fraction within a radius R in observed ellipticals and spheroids increases strongly at small R. At a fixed total stellar mass (or host velocity dispersion), on very small scales near the BH radius of influence, there is an order-of-magnitude scatter in the amount of gas that must have entered and formed stars. In short, the BH appears to ‘know more’ about the global host galaxy potential on large scales than the stars and gas supply on small scales. This is predicted in self-regulated models; however, models where there is no feedback would generically predict order-of-magnitude scatter in the BH–host correlations. Likewise, models in which the BH feedback in the ‘bright’ mode does not regulate the growth of the BH itself, but sets the stellar mass of the galaxy by inducing star formation or blowing out a mass in gas much larger than the galaxy stellar mass, are difficult to reconcile with the scatter on small scales.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".