Impact of Accretion Flow Dynamics on Gas-dynamical Black Hole Mass Estimates
Bibliographic record
Abstract
At low redshift, the majority of supermassive black hole (SMBH) mass estimates are obtained from modeling stellar kinematics or ionized gas dynamics in the vicinity of the galaxy nucleus. For large early type galaxies, stellar kinematics models predict higher masses than gas-dynamical models. In the case of M87, this discrepancy is larger than 2 $σ$. Critical to gas-dynamical modeling is the assumed underlying dynamical state of the gas: that it lies on circular Keplerian orbits, potentially with some additional turbulent pressure support. This is inconsistent with models of the gas flow about low-accretion-rate SMBHs and at odds with observations of the Galactic Center. We present a simple model for non-Keplerian gas disks and explore their implications for SMBH mass measurements. We show that a larger central black hole with gas experiencing small amounts of sub-Keplerian motion can produce velocity curves similar to models that just contain circular Keplerian motions and a lower black hole mass. However, these non-Keplerian models are distinguishable from low-mass Keplerian models primarily through measurements of the velocity dispersion, wherein non-Keplerian models produce higher and narrower peak dispersions. Away from the galaxy center, but still within the circumnuclear gas disk, non-Keplerian models also become distinguishable from Keplerian models via a shift in the velocity curve. The velocity model presented in this paper is capable of resolving the discrepancy between the ionized gas dynamics and stellar kinematics mass estimates, and is applicable to gas-dynamical mass estimates of SMBHs in general.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".