Systematic Difference between Ionized and Molecular Gas Velocity Dispersions in z ∼ 1–2 Disks and Local Analogs
Bibliographic record
Abstract
Abstract We compare the molecular and ionized gas velocity dispersions of nine nearby turbulent disks, analogs to high-redshift galaxies, from the DYNAMO sample using new Atacama Large Millimeter/submillimeter Array and GMOS/Gemini observations. We combine our sample with 12 galaxies at z ∼ 0.5–2.5 from the literature. We find that the resolved velocity dispersion is systematically lower by a factor 2.45 ± 0.38 for the molecular gas compared to the ionized gas, after correcting for thermal broadening. This offset is constant within the galaxy disks and indicates the coexistence of a thin molecular gas disk and a thick ionized one. This result has a direct impact on the Toomre Q and pressure derived in galaxies. We obtain pressures ∼0.22 dex lower on average when using the molecular gas velocity dispersion, σ 0,mol . We find that σ 0,mol increases with gas fraction and star formation rate. We also obtain an increase with redshift and show that the EAGLE and FIRE simulations overall overestimate σ 0,mol at high redshift. Our results suggest that efforts to compare the kinematics of gas using ionized gas as a proxy for the total gas may overestimate the velocity dispersion by a significant amount in galaxies at the peak of cosmic star formation. When using the molecular gas as a tracer, our sample is not consistent with predictions from star formation models with constant efficiency, even when including transport as a source of turbulence. Feedback models with variable star formation efficiency, ϵ ff , and/or feedback efficiency, p * / m * , better predict our observations.
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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 | 0.001 | 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".