STELLAR VELOCITY DISPERSION MEASUREMENTS IN HIGH-LUMINOSITY QUASAR HOSTS AND IMPLICATIONS FOR THE AGN BLACK HOLE MASS SCALE
Bibliographic record
Abstract
We present new stellar velocity dispersion measurements for four luminous quasars with the Near-Infrared Integral Field Spectrometer instrument and the ALTAIR laser guide star adaptive optics system on the Gemini North 8 m telescope. Stellar velocity dispersion measurements and measurements of the supermassive black hole (BH) masses in luminous quasars are necessary to investigate the coevolution of BHs and galaxies, trace the details of accretion, and probe the nature of feedback. We find that higher-luminosity quasars with higher-mass BHs are not offset with respect to the M BH –σ * relation exhibited by lower-luminosity active galactic nuclei (AGNs) with lower-mass BHs, nor do we see correlations with galaxy morphology. As part of this analysis, we have recalculated the virial products for the entire sample of reverberation-mapped AGNs and used these data to redetermine the mean virial factor 〈 f 〉 that places the reverberation data on the quiescent M BH –σ * relation. With our updated measurements and new additions to the AGN sample, we obtain 〈 f 〉 = 4.31 ± 1.05, which is slightly lower than, but consistent with, most previous determinations.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".