GOODS- <i>HERSCHEL</i> : GAS-TO-DUST MASS RATIOS AND CO-TO-H <sub>2</sub> CONVERSION FACTORS IN NORMAL AND STARBURSTING GALAXIES AT HIGH- <i>z</i>
Bibliographic record
Abstract
We explore the gas-to-dust mass ratio ( M gas / M d ) and the CO luminosity-to- M gas conversion factor (α CO ) of two well-studied galaxies in the Great Observatories Origins Deep Survey North field that are expected to have different star-forming modes, the starburst GN20 at z = 4.05 and the normal star-forming galaxy BzK-21000 at z = 1.52. Detailed sampling is available for their Rayleigh–Jeans emission via ground-based millimeter (mm) interferometry (1.1–6.6 mm) along with Herschel PACS and SPIRE data that probe the peak of their infrared emission. Using the physically motivated Draine & Li models, as well as a modified blackbody function, we measure the dust mass ( M dust ) of the sources and find (2.0 +0.7 −0.6 × 10 9 ) M ☉ for GN20 and (8.6 +0.6 −0.9 × 10 8 ) M ☉ for BzK-21000. The addition of mm data reduces the uncertainties of the derived M dust by a factor of ∼2, allowing the use of the local M gas / M d versus metallicity relation to place constraints on the α CO values of the two sources. For GN20 we derive a conversion factor of α CO < 1.0 M ☉ pc −2 (K km s −1 ) −1 , consistent with that of local ultra-luminous infrared galaxies, while for BzK-21000 we find a considerably higher value, α CO ∼4.0 M ☉ pc −2 (K km s −1 ) −1 , in agreement with an independent kinematic derivation reported previously. The implied star formation efficiency is ∼25 L ☉ / M ☉ for BzK-21000, a factor of ∼5–10 lower than that of GN20. The findings for these two sources support the existence of different disk-like and starburst star formation modes in distant galaxies, although a larger sample is required to draw statistically robust results.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".