Cold atomic gas identified by H I self-absorption
Bibliographic record
Abstract
Context. Stars form in the dense interiors of molecular clouds. The dynamics and physical properties of the atomic interstellar medium (ISM) set the conditions under which molecular clouds and eventually stars form. It is, therefore, critical to investigate the relationship between the atomic and molecular gas phase to understand the global star formation process. Aims. Using the high angular resolution data from The H I/OH/Recombination (THOR) line survey of the Milky Way, we aim to constrain the kinematic and physical properties of the cold atomic hydrogen gas phase toward the inner Galactic plane. Methods. H I self-absorption (HISA) has proven to be a viable method to detect cold atomic hydrogen clouds in the Galactic plane. With the help of a newly developed self-absorption extraction routine (astroSABER), we built upon previous case studies to identify H I self-absorption toward a sample of giant molecular filaments (GMFs). Results. We find the cold atomic gas to be spatially correlated with the molecular gas on a global scale. The column densities of the cold atomic gas traced by HISA are usually on the order of 1020 cm−2 whereas those of molecular hydrogen traced by 13CO are at least an order of magnitude higher. The HISA column densities are attributed to a cold gas component that accounts for a fraction of ~5% of the total atomic gas budget within the clouds. The HISA column density distributions show pronounced log-normal shapes that are broader than those traced by H I emission. The cold atomic gas is found to be moderately supersonic with Mach numbers of approximately a few. In contrast, highly supersonic dynamics drive the molecular gas within most filaments. Conclusions. While H I self-absorption is likely to trace just a small fraction of the total cold neutral medium within a cloud, probing the cold atomic ISM by the means of self-absorption significantly improves our understanding of the dynamical and physical interaction between the atomic and molecular gas phase during cloud formation.
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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 | 0.000 | 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".