A lack of constraints on the cold opaque H <scp>i</scp> mass: H <scp>i</scp> spectra in M31 and M33 prefer multicomponent models over a single cold opaque component
Bibliographic record
Abstract
ABSTRACT Previous work has argued that atomic gas mass estimates of galaxies from 21-cm H i emission are systematically low due to a cold opaque atomic gas component. If true, this opaque component necessitates a $\sim 35{{\ \rm per\ cent}}$ correction factor relative to the mass from assuming optically thin H i emission. These mass corrections are based on fitting H i spectra with a single opaque component model that produces a distinct ‘top-hat’ shaped line profile. Here, we investigate this issue using deep, high spectral resolution H i VLA observations of M31 and M33 to test if these top-hat profiles are instead superpositions of multiple H i components along the line of sight. We fit both models and find that ${\gt}80{{\ \rm per\ cent}}$ of the spectra strongly prefer a multicomponent Gaussian model while ${\lt}2{{\ \rm per\ cent}}$ prefer the single opacity-corrected component model. This strong preference for multiple components argues against previous findings of lines of sight dominated by only cold H i. Our findings are enabled by the improved spectral resolution (0.42 ${\rm km\, s^{-1}}$), whereas coarser spectral resolution blends multiple components together. We also show that the inferred opaque atomic ISM mass strongly depends on the goodness-of-fit definition and is highly uncertain when the inferred spin temperature has a large uncertainty. Finally, we find that the relation of the H i surface density with the dust surface density and extinction has significantly more scatter when the inferred H i opacity correction is applied. These variations are difficult to explain without additionally requiring large variations in the dust properties. Based on these findings, we suggest that the opaque H i mass is best constrained by H i absorption studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".