Supermassive black holes in a mass-limited galaxy sample
Bibliographic record
Abstract
ABSTRACT The observed scaling relations between supermassive black hole masses and their host galaxy properties indicate that supermassive black holes influence the evolution of galaxies. However, the scaling relations may be affected by selection biases. We propose to measure black hole masses in a mass-limited galaxy sample including all non-detections to inprove constraints on galaxy mass – black hole mass scaling relations and test for selection bias. We use high-spatial resolution spectroscopy from the Keck and Gemini telescopes, and the Jeans Anisotropic Modelling method to measure black hole masses in early-type galaxies from the Virgo Cluster. We present four new black hole masses and one upper limit in our mass-selected sample of galaxies of galaxy mass (1.0–3.2) $\times 10^{10} \, \mathrm{M}_\odot$. This brings the total measured to 11 galaxies out of a full sample of 18 galaxies, allowing us to constrain scaling relations. We calculate a lower limit for the average black hole mass in our sample of $3.7 \times 10^{7} \, \mathrm{M}_\odot$. This is at an average galaxy stellar mass of $(1.81 \pm 0.14)\times 10^{10} \, \mathrm{M}_\odot$ and an average bulge mass of $(1.31 \pm 0.15) \times 10^{10} \, \mathrm{M}_\odot$. This lower limit shows that black hole masses in early-type galaxies are not strongly affected by selection biases.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".