VALIDATION OF THE EQUILIBRIUM MODEL FOR GALAXY EVOLUTION TO<i>z</i>∼ 3 THROUGH MOLECULAR GAS AND DUST OBSERVATIONS OF LENSED STAR-FORMING GALAXIES
Bibliographic record
Abstract
We combine IRAM Plateau de Bure Interferometer and Herschel PACS and SPIRE measurements to study the dust and gas contents of high-redshift star-forming galaxies. We present new observations for a sample of 17 lensed galaxies at z = 1.4–3.1, which allow us to directly probe the cold interstellar medium of normal star-forming galaxies with stellar masses of ∼10 10 M ☉ , a regime otherwise not (yet) accessible by individual detections in Herschel and molecular gas studies. The lensed galaxies are combined with reference samples of submillimeter and normal z ∼ 1–2 star-forming galaxies with similar far-infrared photometry to study the gas and dust properties of galaxies in the SFR– M * –redshift parameter space. The mean gas depletion timescale of main-sequence (MS) galaxies at z > 2 is measured to be only ∼450 Myr, a factor of ∼1.5 (∼5) shorter than at z = 1 ( z = 0), in agreement with a (1 + z ) −1 scaling. The mean gas mass fraction at z = 2.8 is 40% ± 15% (44% after incompleteness correction), suggesting a flattening or even a reversal of the trend of increasing gas fractions with redshift recently observed up to z ∼ 2. The depletion timescale and gas fractions of the z > 2 normal star-forming galaxies can be explained under the "equilibrium model" for galaxy evolution, in which the gas reservoir of galaxies is the primary driver of the redshift evolution of specific star formation rates. Due to their high star formation efficiencies and low metallicities, the z > 2 lensed galaxies have warm dust despite being located on the star formation MS. At fixed metallicity, they also have a gas-to-dust ratio 1.7 times larger than observed locally when using the same standard techniques, suggesting that applying the local calibration of the δ GDR –metallicity relation to infer the molecular gas mass of high-redshift galaxies may lead to systematic differences with CO-based estimates.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".