Study of H <scp>i</scp> Turbulence in the SMC Using Multipoint Structure Functions
Bibliographic record
Abstract
Abstract Turbulence in the interstellar medium (ISM) plays an important role in many physical processes, including forming stars and shaping complex ISM structures. In this work, we investigate the H i turbulence properties of the Small Magellanic Cloud (SMC) to reveal what physical mechanisms drive the turbulence and at what scales. Using high-resolution H i data from the Galactic ASKAP survey and multipoint structure functions (SFs), we perform a statistical analysis of H i turbulence in 34 subregions of the SMC. The two-point SFs tend to show a linear trend, and their slope values are relatively uniform across the SMC, suggesting that large-scale structures exist and are dominant in the two-point SFs. On the other hand, seven-point SFs enable us to probe small-scale turbulence by removing large-scale fluctuations, which is difficult to achieve with the two-point SFs. In the seven-point SFs, we find break features at scales of 34–84 pc, with a median scale of ∼50 pc. This result indicates the presence of small-scale turbulence fluctuations in the SMC and quantifies their scale. In addition, we find strong correlations between the slope values of the seven-point SFs and stellar-feedback-related quantities (e.g., H α intensity, the number of young stellar objects, and the number of H i shells), suggesting that stellar feedback may affect the small-scale turbulence properties of the H i gas in the SMC. Lastly, the estimated sonic Mach numbers across the SMC are subsonic, which is consistent with the fact that the H i gas of the SMC primarily consists of a warm neutral medium.
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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 | 0.000 | 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".