Cloud structures in M 17 SWex : Possible cloud–cloud collision
Bibliographic record
Abstract
Abstract Using wide-field 13CO (J = 1−0) data taken with the Nobeyama 45 m telescope, we investigate cloud structures of the infrared dark cloud complex in M 17 with Spectral Clustering for Interstellar Molecular Emission Segmentation. In total, we identified 118 clouds that include 11 large clouds with radii larger than 1 pc. The clouds are mainly distributed in the two representative velocity ranges of 10–20 km s−1 and 30–40 km s−1. By comparing this with the ATLASGAL catalog, we found that the majority of the 13CO clouds with 10–20 km s−1 and 30–40 km s−1 are likely located at distances of 2 kpc (Sagittarius arm) and 3 kpc (Scutum arm), respectively. Analyzing the spatial configuration of the identified clouds and their velocity structures, we attempt to reveal the origin of the cloud structure in this region. Here we discuss three possibilities: (1) overlapping with different velocities, (2) cloud oscillation, and (3) cloud–cloud collision. In the position–velocity diagrams, we found spatially extended faint emission between ∼20 km s−1 and ∼35 km s−1, which is mainly distributed in the spatially overlapped areas of the clouds. Additionally, the cloud complex system is unlikely to be gravitationally bound. We also found that in some areas where clouds with different velocities overlapped, the magnetic field orientation changes abruptly. The distribution of the diffuse emission in the position–position–velocity space and the bending magnetic fields appear to favor the cloud–cloud collision scenario compared to other scenarios. In the cloud–cloud collision scenario, we propose that two ∼35 km s−1 foreground clouds are colliding with clouds at ∼20 km s−1 with a relative velocity of 15 km s−1. These clouds may be substructures of two larger clouds having velocities of ∼35 km s−1 (≳103 M⊙) and ∼20 km s−1 (≳104 M⊙), respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".