Measuring the properties of homogeneous turbulence in curved space–times
Bibliographic record
Abstract
ABSTRACT Turbulence in curved space–times in general, and in the vicinity of black holes (BHs) in particular, represents a poorly understood phenomenon that is often analysed employing techniques developed for flat space–times. We here propose a novel approach to study turbulence in strong gravitational fields that is based on the computation of structure functions on generic manifolds and is thus applicable to arbitrary curved space–times. In particular, we introduce, for the first time, a formalism to compute the characteristic properties of turbulence, such as the second-order structure function or the power spectral density, in terms of proper lengths and volumes and not in terms of coordinate lengths and volumes, as customarily done. By applying the new approach to the turbulent rest-mass density field from simulations of magnetized disc accretion onto a Kerr BH, we inspect in a rigorous way turbulence in regions close to the event horizon, but also in the disc, the wind, and the jet. We demonstrate that the new approach can capture the typical behaviour of an inertial-range cascade and that differences up to $40\!-\!80 {{\, \rm \%}}$ emerge in the vicinity of the event horizon with respect to the standard flat space–time approach. While these differences become smaller at larger distances, our study highlights that special care needs to be paid when analysing turbulence in strongly curved space–times.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".