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-ultraviolet (FUV) 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 FUV emission that reaches the GMC from the OB association and of the excess 21-cm radio H i emission produced after these FUV photons convert H2 into H i 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 densities well below the critical density for CO(1–0) emission. We describe our ‘PDR method’ in more detail and apply it using GALEX FUV and Very Large Array 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 the galactocentric radius; these results are generally similar to those obtained earlier in the cases of 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".