The EDGE-CALIFA Survey: An integral field unit-based integrated molecular gas database for galaxy evolution studies in the Local Universe
Bibliographic record
Abstract
Studying galaxy evolution requires knowledge not only of the stellar properties, but also of the interstellar medium (in particular the molecular phase) out of which stars form, using a statistically significant and unbiased sample of galaxies. To this end, we introduce here the integrated Extragalactic Database for Galaxy Evolution (iEDGE), a collection of integrated stellar and nebular emission lines, and molecular gas properties from 643 galaxies in the local Universe. These galaxies are drawn from the CALIFA datasets, and are followed up in CO lines by the APEX, CARMA, and ACA telescopes. As this database is assembled from data coming from a heterogeneous set of telescopes (including IFU optical data and single-dish and interferometric CO data), we adopted a series of techniques (tapering, spatial and spectral smoothing, and aperture correction) to homogenise the data. Due to the application of these techniques, the database contains measurements from the inner regions of the galaxies and for the full galaxy extent. We used the database to study the fundamental star formation relationships between star formation rate (SFR), stellar mass (M*), and molecular gas mass (Mmol) across galaxies with different morphologies. We observed that the diagrams defined by these quantities are bi-modal, with early-type passive objects well separated from spiral star-forming galaxies. Additionally, while the molecular gas fraction (fmol = Mmol/M*) decreases homogeneously across these two types of galaxies, the star formation efficiency (SFE=SFR/Mmol) in the inner regions of passive galaxies is almost two orders of magnitude lower compared to the global values. This indicates that inside-out quenching requires not only low fmol, but also strongly reduced SFE in the galactic centres.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".