Wind-fed GRMHD simulations of Sagittarius A*: tilt and alignment of jets and accretion discs, electron thermodynamics, and multiscale modelling of the rotation measure
Bibliographic record
Abstract
ABSTRACT Wind-fed models offer a unique way to form predictive models of the accretion flow surrounding Sagittarius A*. We present 3D wind-fed magnetohydrodynamic (MHD) and general relativistic magnetohydrodynamic (GRMHD) simulations spanning the entire dynamic range of accretion from parsec scales to the event horizon. We expand on previous work by including non-zero black hole spin and dynamically evolved electron thermodynamics. Initial conditions for these simulations are generated from simulations of the observed Wolf–Rayet stellar winds in the Galactic Centre. The resulting flow tends to be highly magnetized (β ≈ 2) with an ∼r−1 density profile independent of the strength of magnetic fields in the winds. Our simulations reach the magnetically arrested disc (MAD) state for some, but not all cases. In tilted flows, standard and normal evolution (SANE) jets tend to align with the angular momentum of the gas at large scales, even if that direction is perpendicular to the black hole spin axis. Conversely, MAD jets tend to align with the black hole spin axis. The gas angular momentum shows similar behaviour: SANE flows tend to only partially align while MAD flows tend to fully align. With a limited number of dynamical free parameters, our models can produce accretion rates, 230 GHz flux, and unresolved linear polarization fractions roughly consistent with observations for several choices of electron heating fraction. Absent another source of large-scale magnetic field, winds with a higher degree of magnetization (e.g. where the magnetic pressure is 1/100 of the ram pressure in the winds) may be required to get a sufficiently large rotation measure with consistent sign.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".