VALES – IV. Exploring the transition of star formation efficiencies between normal and starburst galaxies using APEX/SEPIA Band-5 and ALMA at low redshift
Bibliographic record
Abstract
In this work, we present new the Swedish-ESO PI receiver for the Atacama Pathfinder Experiment APEX/SEPIA Band-5 observations targeting the CO (J = 2–1) emission line of 24 Herschel-detected galaxies at z = 0.1–0.2. Combining this sample with our recent new Valparaíso ALMA Line Emission Survey (VALES), we investigate the star formation efficiencies [SFEs = star formation rate (SFR)/|$M_{\rm H_{2}}$|] of galaxies at low redshift. We find the SFE of our sample bridges the gap between normal star-forming galaxies and Ultra-Luminous Infrared Galaxies (ULIRGs), which are thought to be triggered by different star formation modes. Considering the SFE΄ as the SFR and the |$L^{\prime }_{\rm CO}$| ratio, our data show a continuous and smooth increment as a function of infrared luminosity (or star formation rate) with a scatter about 0.5 dex, instead of a steep jump with a bimodal behaviour. This result is due to the use of a sample with a much larger range of sSFR/sSFRms using LIRGs, with luminosities covering the range between normal and ULIRGs. We conclude that the main parameters controlling the scatter of the SFE in star-forming galaxies are the systematic uncertainty of the αCO conversion factor, the gas fraction, and physical size.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".