Dust temperature and CO → H<sub>2</sub>conversion factor variations in the SFR-<i>M</i><sub>∗</sub>plane
Bibliographic record
Abstract
Deep Herschel PACS/SPIRE imaging and 12CO(2−1) line luminosities from the IRAM Plateau de Bure Interferometer are combined for a sample of 17 galaxies at z > 1 from the GOODS-N field. The sample includes galaxies both on and above the main sequence (MS) traced by star-forming galaxies in the SFR-M∗ plane. The far-infrared data are used to derive dust masses, Mdust, following the Draine & Li (2007, ApJ, 657, 810) models. Combined with an empirical prescription for the dependence of the gas-to-dust ratio on metallicity (δGDR(μ0)), the CO luminosities and Mdust values are used to derive for each galaxy the CO-to-H2 conversion factor, αCO. Like in the local Universe, the value of αCO is a factor of ~5 smaller in starbursts compared to normal star-forming galaxies (SFGs). We additionally uncover a relation between αCO and dust temperature (Tdust; αCO decreasing with increasing Tdust) as obtained from modified blackbody fits to the far-infrared data. While the absolute normalization of the αCO(Tdust) relation is uncertain, the global trend is robust against possible systematic biases in the determination of Mdust, δGDR(μ0) or metallicity. Although we cannot formally distinguish between a step and a smooth evolution of αCO with the dust temperature, we can unambiguously conclude that in galaxies of near-solar metallicity, a critical value of Tdust = 30 K can be used to determine whether the appropriate αCO is closer to the “starburst” value (1.0 M⊙ (K km s-1 pc2)-1, when Tdust > 30 K) or closer to the Galactic value (4.35 M⊙ (K km s-1 pc2)-1, when Tdust < 30 K). This indicator has the great advantage of being less subjective than visual morphological classifications of mergers/SFGs, which can be difficult at high z because of the clumpy nature of SFGs. Using Tdust to select the appropriate αCO is also more indicative of ISM conditions than a fixed LIR criterion. In the absence of far-infrared data, the offset of a galaxy from the star formation main sequence (i.e., Δlog (SSFR)MS = log [SSFR(galaxy)/SSFRMS(M∗,z)]) can be used to identify galaxies requiring the use of an αCO conversion factor lower than the Galactic value (i.e., when Δlog (SSFR)MS ≳ 0.3 dex).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".