MASCOT: an ESO–ARO legacy survey of molecular gas in nearby SDSS-MaNGA galaxies – I. First data release, and global and resolved relations between H2 and stellar content
Bibliographic record
Abstract
ABSTRACT We present the first data release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA)–Arizona Radio Observatory (ARO) Survey of CO Targets (MASCOT), a European Southern Observatory public spectroscopic survey conducted at the ARO. We measure the CO(1–0) line emission in a sample of 187 nearby galaxies selected from the MaNGA survey that has obtained integral field unit (IFU) spectroscopy for a sample of ∼10 000 galaxies at low redshift. The main goal of MASCOT is to probe the molecular gas content of star-forming galaxies with stellar masses >109.5 M⊙ and with associated MaNGA IFU observations and well-constrained quantities like stellar masses, star formation rates, and metallicities. In this paper, we present the first results of the MASCOT survey, providing integrated CO(1–0) measurements that cover several effective radii of the galaxy and present CO luminosities, CO kinematics, and estimated H2 gas masses. We observe that the decline of the galaxy star formation rate with respect to the star formation main sequence increases with the decrease of molecular gas and with a reduced star formation efficiency, in agreement with results of other integrated studies. Relating the molecular gas mass fractions with the slope of the stellar age gradients inferred from the MaNGA observations, we find that galaxies with lower molecular gas mass fractions tend to show older stellar populations close to the galactic centre, while the opposite is true for galaxies with higher molecular gas mass fractions, providing tentative evidence for inside-out quenching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".