THE RADIATIVE EFFICIENCY OF ACCRETION FLOWS IN INDIVIDUAL ACTIVE GALACTIC NUCLEI
Bibliographic record
Abstract
The radiative efficiency of active galactic nucleus (AGN) is commonly estimated based on the total mass accreted and the total AGN light emitted per unit volume in the universe integrated over time (the Soltan argument). In individual AGNs, thin accretion disk model spectral fits can be used to deduce the absolute accretion rate , if the black hole mass M is known. The radiative efficiency η is then set by the ratio of the bolometric luminosity L bol to . We apply this method to determine η in a sample of 80 Palomar–Green quasars with well-determined L bol , where is set by thin accretion disk model fits to the optical luminosity density, and the M determination based on the bulge stellar velocity dispersion (13 objects) or the broad line region. We derive a mean log η = −1.05 ± 0.52 consistent with the Soltan-argument-based estimates. We find a strong correlation of η with M , rising from η ∼ 0.03 at M = 10 7 M ☉ and L / L Edd ∼ 1 to η ∼ 0.4 at M = 10 9 M ☉ and L / L Edd ∼ 0.3. This trend is related to the overall uniformity of L opt / L bol in our sample, particularly the lack of the expected increase in L opt / L bol with increasing M (and decreasing L / L Edd ), which is a generic property of thermal disk emission at fixed η. The significant uncertainty in the M determination is not large enough to remove the correlation. The rising η with M may imply a rise in the black hole spin with M , as proposed based on other indirect arguments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".