ESTIMATING THE PARAMETERS OF SAGITTARIUS A*'s ACCRETION FLOW VIA MILLIMETER VLBI
Bibliographic record
Abstract
Recent millimeter-VLBI observations of Sagittarius A* (Sgr A*) have, for the first time, directly probed distances comparable to the horizon scale of a black hole. This provides unprecedented access to the environment immediately around the horizon of an accreting black hole. We leverage both existing spectral and polarization measurements and our present understanding of accretion theory to produce a suite of generic radiatively inefficient accretion flow (RIAF) models of Sgr A*, which we then fit to these recent millimeter-VLBI observations. We find that if the accretion flow onto Sgr A* is well described by an RIAF model, the orientation and magnitude of the black hole's spin are constrained to a two-dimensional surface in the spin, inclination, position angle parameter space. For each of these, we find the likeliest values and their 1σ and 2σ errors to be a = 0 +0.4+0.7 , , and , when the resulting probability distribution is marginalized over the others. The most probable combination is a = 0 +0.2+0.4 , , and , though the uncertainties on these are very strongly correlated, and high probability configurations exist for a variety of inclination angles above 30° and spins below 0.99. Nevertheless, this demonstrates the ability millimeter-VLBI observations, even with only a few stations, to significantly constrain the properties of Sgr A*.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".