HIERARCHICAL STRUCTURE OF MAGNETOHYDRODYNAMIC TURBULENCE IN POSITION-POSITION-VELOCITY SPACE
Bibliographic record
Abstract
Magnetohydrodynamic turbulence is able to create hierarchical structures in the interstellar medium (ISM) that are correlated on a wide range of scales via the energy cascade. We use hierarchical tree diagrams known as dendrograms to characterize structures in synthetic position–position–velocity (PPV) emission cubes of isothermal magnetohydrodynamic turbulence. We show that the structures and degree of hierarchy observed in PPV space are related to the presence of self-gravity and the global sonic and Alfvénic Mach numbers. Simulations with higher Alfvénic Mach number, self-gravity and supersonic flows display enhanced hierarchical structure. We observe a strong dependency on the sonic and Alfvénic Mach numbers and self-gravity when we apply the statistical moments (i.e., mean, variance, skewness, kurtosis) to the leaf and node distribution of the dendrogram. Simulations with self-gravity, larger magnetic field and higher sonic Mach number have dendrogram distributions with higher statistical moments. Application of the dendrogram to three-dimensional density cubes, also known as position–position–position (PPP) cubes, reveals that the dominant emission contours in PPP and PPV are related for supersonic gas but not for subsonic. We also explore the effects of smoothing, thermal broadening, and velocity resolution on the dendrograms in order to make our study more applicable to observational data. These results all point to hierarchical tree diagrams as being a promising additional tool for studying ISM turbulence and star forming regions for obtaining information on the degree of self-gravity, the Mach numbers and the complicated relationship between PPV and PPP data.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".