Atomic Hydrogen produced in M33 Photodissociation Regions
Bibliographic record
Abstract
We derive total (atomic + molecular) hydrogen densities in giant molecular clouds (GMCs) in the nearby spiral galaxy M33 using a method that views the atomic hydrogen near regions of recent star formation as the product of photodissociation. Far-UV photons emanating from a nearby OB association produce a layer of atomic hydrogen on the surfaces of nearby GMCs. Our approach provides an estimate of the total hydrogen density in these GMCs from observations of the excess far-UV emission that reaches the GMC from the OB association, and the excess 21-cm radio HI emission produced after these far-UV photons convert H2 into HI on the GMC surface. The method provides an alternative approach to the use of CO emission as a tracer of H2 in GMCs, and is especially sensitive to a range of density well below the critical density for CO(1-0) emission. We describe our "PDR method" in more detail and apply it using GALEX far-UV and VLA 21-cm radio data to obtain volume densities in a selection of GMCs in the nearby spiral galaxy M33. We have also examined the sensitivity of the method to the linear resolution of the observations used; the results obtained at 20 pc are similar to those for the larger set of data at 80 pc resolution. The cloud densities we derive range from 1 to 500 cm-3, with no clear dependence on galactocentric radius; these results are generally similar to those obtained earlier in M81, M83, and M101 using the same method.
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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".