Hydrogen volume densities in nearby galaxies – I. An automated approach
Bibliographic record
Abstract
Using a simple model of photodissociated atomic hydrogen on a galactic scale, it is possible to derive total hydrogen volume densities. These densities, obtained through a combination of atomic hydrogen, far-ultraviolet and metallicity data, provide an independent probe of the combined atomic and molecular hydrogen gas in galactic discs. We present a new, flexible and fully automated procedure using this simple model. This automated method will allow us to take full advantage of a host of available data on galaxies in order to calculate the total hydrogen volume densities of the giant molecular clouds surrounding sites of recent star formation. Until now this was only possible on a galaxy-by-galaxy basis using by-eye analysis of candidate photodissociation regions. We test the automated method by adopting various models for the dust-to-gas ratio and comparing the resulting densities for M74, including a new metallicity map of M74 produced by integral field spectroscopy. We test the procedure against previously published M83 volume densities based on the same method and find no significant differences. The range of total hydrogen volume densities obtained for M74 is approximately 5–700 cm−3. Different dust-to-gas ratio models do not result in measurably different densities. The cloud densities presented here mean that M74 is added to the list of galaxies analysed using the assumption of photodissociated atomic hydrogen occurring near sites of recent star formation, and consolidate the method. For the first time, full metallicity maps are included in the analysis as opposed to metallicity gradients. The results will need to be compared with other tracers of the interstellar medium and photodissociation regions, such as CO and C ii, in order to test our basic assumptions, specifically our assumption that the H i we detect originates in photodissociation regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".