PHIBSS: exploring the dependence of the CO–H2 conversion factor on total mass surface density at z<1.5
Bibliographic record
Abstract
We present an analysis of the relationship between the CO–H2 conversion factor (αCO) and total mass surface density (Σtot) in star-forming galaxies at z < 1.5. Our sample, which is drawn from the IRAM Plateau de Bure HIgh-z Blue Sequence Survey (PHIBSS) and the CO Legacy Database for GASS (COLD GASS), includes ‘normal’, massive star-forming galaxies that dominate the evolution of the cosmic star formation rate (SFR) at this epoch and probe the Σtot regime where the strongest variation in αCO is observed. We constrain αCO via existing CO observations, measurements of the SFR and an assumed molecular gas depletion time (tdep = Mgas/SFR) – the latter two of which establish the total molecular gas mass independent of the observed CO luminosity. For a broad range of adopted depletion times, we find that αCO is independent of total mass surface density, with little deviation from the canonical Milky Way value. This runs contrary to a scenario in which αCO decreases as surface density increases within the extended clouds of molecular gas that potentially fuel clumps of star formation in z ∼ 1 galaxies, similar to those observed in local ultra-luminous infrared galaxies. Instead, our results suggest that molecular gas, at both z ∼ 0 and z ∼ 1, is primarily in the form of self-gravitating molecular clouds. While CO observations suggest a factor of ∼3 reduction in the average molecular gas depletion time between z ∼ 0 and z ∼ 1, we find that, for typical galaxies, the structure of molecular gas and the process of star formation at z ∼ 1 is otherwise remarkably similar to that observed in local star-forming systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".