Estimating the Molecular Gas Mass of Low-redshift Galaxies from a Combination of Mid-infrared Luminosity and Optical Properties
Bibliographic record
Abstract
Abstract We present CO ( J = 1−0) and/or CO ( J = 2−1) spectroscopy for 31 galaxies selected from the ongoing Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, obtained with multiple telescopes. This sample is combined with CO observations from the literature to study the correlation of the CO luminosities ( ) with the mid-infrared luminosities at 12 ( ) and 22 μ m ( ), as well as the dependence of the residuals on a variety of galaxy properties. The correlation with is tighter and more linear, but galaxies with relatively low stellar masses ( M ⊙ ) and blue colors ( and/or NUV − r ≲ 3) fall significantly below the mean – relation. We propose a new estimator of the CO (1−0) luminosity (and thus the total molecular gas mass M mol ) that is a linear combination of three parameters: , , and g − r . We show that, with a scatter of only 0.18 dex in log , this estimator provides unbiased estimates for galaxies of different properties and types. An immediate application of this estimator to a compiled sample of galaxies with only CO ( J = 2−1) observations yields a distribution of the CO ( J = 2−1) to CO ( J = 1−0) luminosity ratios ( R21 ) that agrees well with the distribution of real observations, in terms of both the median and the shape. Application of our estimator to the current MaNGA sample reveals a gas-poor population of galaxies that are predominantly early-type and show no correlation between molecular gas-to-stellar mass ratio and star formation rate, in contrast to gas-rich galaxies. We also provide alternative estimators with similar scatters, based on r - and/or z -band luminosities instead of . These estimators serve as cheap and convenient M mol proxies to be potentially applied to large samples of galaxies, thus allowing statistical studies of gas-related processes of galaxies.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".