Fragmentation and kinematics in high-mass star formation
Bibliographic record
Abstract
Context. The formation of high-mass star-forming regions from their parental gas cloud and the subsequent fragmentation processes lie at the heart of star formation research. Aims. We aim to study the dynamical and fragmentation properties at very early evolutionary stages of high-mass star formation. Methods. Employing the NOrthern Extended Millimeter Array and the IRAM 30 m telescope, we observed two young high-mass star-forming regions, ISOSS22478 and ISOSS23053, in the 1.3 mm continuum and spectral line emission at a high angular resolution (~0.8″). Results. We resolved 29 cores that are mostly located along filament-like structures. Depending on the temperature assumption, these cores follow a mass-size relation of approximately M ∝ r2.0 ± 0.3, corresponding to constant mean column densities. However, with different temperature assumptions, a steeper mass-size relation up to M ∝ r3.0 ± 0.2, which would be more likely to correspond to constant mean volume densities, cannot be ruled out. The correlation of the core masses with their nearest neighbor separations is consistent with thermal Jeans fragmentation. We found hardly any core separations at the spatial resolution limit, indicating that the data resolve the large-scale fragmentation well. Although the kinematics of the two regions appear very different at first sight – multiple velocity components along filaments in ISOSS22478 versus a steep velocity gradient of more than 50 km s−1 pc−1 in ISOSS23053 – the findings can all be explained within the framework of a dynamical cloud collapse scenario. Conclusions. While our data are consistent with a dynamical cloud collapse scenario and subsequent thermal Jeans fragmentation, the importance of additional environmental properties, such as the magnetization of the gas or external shocks triggering converging gas flows, is nonetheless not as well constrained and would require future investigation.
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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) | 0.001 | 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".