A new method for spatially resolving the turbulence-driving mixture in the ISM with application to the Small Magellanic Cloud
Bibliographic record
Abstract
ABSTRACT Turbulence plays a crucial role in shaping the structure of the interstellar medium. The ratio of the three-dimensional density contrast ($\sigma _{\rho /\rho _0}$) to the turbulent sonic Mach number ($\mathcal {M}$) of an isothermal, compressible gas describes the ratio of solenoidal to compressive modes in the turbulent acceleration field of the gas, and is parameterized by the turbulence driving parameter: $b=\sigma _{\rho /\rho _0}/\mathcal {M}$. The turbulence driving parameter ranges from b = 1/3 (purely solenoidal) to b = 1 (purely compressive), with b = 0.38 characterizing the natural mixture (1/3 compressive, 2/3 solenoidal) of the two driving modes. Here, we present a new method for recovering $\sigma _{\rho /\rho _0}$, $\mathcal {M}$, and b, from observations on galactic scales, using a roving kernel to produce maps of these quantities from column density and centroid velocity maps. We apply our method to high-resolution ${\rm H}\,\rm{\small I}$ emission observations of the Small Magellanic Cloud (SMC) from the GASKAP-HI survey. We find that the turbulence driving parameter varies between b ∼ 0.3 and 1.0 within the main body of the SMC, but the median value converges to b ∼ 0.51, suggesting that the turbulence is overall driven more compressively (b > 0.38). We observe no correlation between the b parameter and ${\rm H}\,\rm{\small I}$ or H α intensity, indicating that compressive driving of ${\rm H}\,\rm{\small I}$ turbulence cannot be determined solely by observing ${\rm H}\,\rm{\small I}$ or H α emission density, and that velocity information must also be considered. Further investigation is required to link our findings to potential driving mechanisms such as star-formation feedback, gravitational collapse, or cloud–cloud collisions.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".