The Galactic H ii Region Luminosity Function at Radio and Infrared Wavelengths
Bibliographic record
Abstract
Abstract The Galactic H ii region luminosity function (LF) is an important metric for understanding global star formation properties of the Milky Way, but only a few studies have been done, and all use relatively small numbers of H ii regions. We use a sample of 797 first Galactic quadrant H ii regions compiled from the Wide-field Infrared Survey Explorer Catalog of Galactic H ii Regions to examine the form of the LF at multiple infrared and radio wavelengths. Our sample is statistically complete for all regions powered by single stars of type O9.5V and earlier. We fit the LF at each wavelength with single and double power laws. Averaging the results from all wavelengths, the mean of the best-fit single power-law index is 〈α〉 = −1.75 ± 0.01. The mean best-fit double power-law indices are 〈α 1〉 = −1.40 ± 0.03 and 〈α 2〉 = −2.33 ± 0.04. We conclude that neither a single nor a double power law is strongly favored over the other. The LFs show some variation when we separate the H ii region sample into subsets by heliocentric distance, physical size, Galactocentric radius, and location relative to the spiral arms, but blending individual H ii regions into larger complexes does not change the value of the power-law indices of the best-fit LF models. The consistency of the power-law indices across multiple wavelengths suggests that the LF is independent of wavelength. This implies that infrared and radio tracers can be employed in place of Hα.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".