CO Molecular Gas in Infrared‐luminous Galaxies
Bibliographic record
Abstract
We present the first statistical survey of the properties of the 12 CO(1-0) and 12 CO(3-2) line emission from the nuclei of a nearly complete subsample of 60 infrared (IR) luminous galaxies selected from SCUBA Local Universe Galaxy Survey (SLUGS). This subsample is flux limited at S 60 μm ≥ 5.24 Jy with far-IR (FIR) luminosities mostly at L FIR > 10 10 L ☉ . We compare the emission line strengths of 12 CO(1-0) and (3-2) transitions at a common resolution of ~15''. The measured 12 CO(3-2) to (1-0) line intensity ratios r 31 vary from 0.22 to 1.72, with a mean value of 0.66 for the sources observed, indicating a large spread of the degree of excitation of CO in the sample. These CO data, together with a wide range of data at different wavelengths obtained from the literature, allow us to study the relationship between the CO excitation conditions and the physical properties of gas/dust and star formation in the central regions of galaxies. Our analysis shows that there is a nonlinear relation between CO and FIR luminosities, such that their ratio L CO / L FIR decreases linearly with increasing L FIR . This behavior was found to be consistent with the Schmidt law relating star formation rate to molecular gas content, with an index N = 1.4 ± 0.3. We also find a possible dependence of the degree of CO gas excitation on the efficiency of star-forming activity. Using the large velocity gradient (LVG) approximation to model the observed data, we investigate the CO-to-H 2 conversion factor X for the SLUGS sample. The results show that the mean value of X for the SLUGS sample is lower by a factor of 10 compared to the conventional value derived for the Galaxy, if we assume the abundance of CO relative to H 2 , Z CO = 10 -4 . For a subset of 12 galaxies with H I maps, we derive a mean total face-on surface density of H 2 + H of about 42 M ☉ pc -2 within about 2 kpc of the nucleus. This value is intermediate between that in galaxies like our own and those with strong star formation.
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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 | 0.001 |
| 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 | 0.000 |
| 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".