A Neutral Hydrogen Absorption Study of Cold Gas in the Outskirts of the Magellanic Clouds Using the GASKAP-H <scp>i</scp> Survey
Bibliographic record
Abstract
Abstract Cold neutral hydrogen (H i) is a crucial precursor for molecular gas formation and can be studied via H i absorption. This study investigates H i absorption in low column density regions of the Small and Large Magellanic Clouds (SMC and LMC) using the Galactic-ASKAP H i (GASKAP-H i) survey, conducted by the Australian Square Kilometer Array Pathfinder (ASKAP). We select 10 SMC directions in the outer regions and 18 LMC directions, with four in the outskirts and 14 within the main disk. Using the radiative transfer method, we decompose the emission and absorption spectra into individual cold neutral medium (CNM) and warm neutral medium (WNM) components. In the SMC, we find H i peak optical depths of 0.09–1.16, spin temperatures of ∼20–50 K, and CNM fractions of 1%–11%. In the LMC, optical depths range from 0.03–3.55, spin temperatures from ∼10–100 K, and CNM fractions from 1%–100%. The SMC’s low CNM fractions likely result from its low metallicity and large LOS depth. Additionally, the SMC’s outskirts show lower CNM fractions than the main body, potentially due to increased CNM evaporation influenced by the hot Magellanic Corona. Shell motions dominate the kinematics of the majority of CNM clouds in this study and likely supply cold H i to the Magellanic Stream. In the LMC, high CNM fraction clouds are found near supergiant shells, where thermal instability induced by stellar feedback promotes WNM-to-CNM transition. Although no carbon monoxide has been detected, enhanced dust shielding in these areas helps maintain the cold H i.
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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".