PHANGS-ALMA: Arcsecond CO(2-1) Imaging of Nearby Star-forming Galaxies
Bibliographic record
Abstract
We present PHANGS-ALMA, the first survey to map CO J = 2 -> 1 line emission at similar to 1 '' similar to 100 pc spatial resolution from a representative sample of 90 nearby (d less than or similar to 20 Mpc) galaxies that lie on or near the z = 0 "main sequence" of star-forming galaxies. CO line emission traces the bulk distribution of molecular gas, which is the cold, star-forming phase of the interstellar medium. At the resolution achieved by PHANGS-ALMA, each beam reaches the size of a typical individual giant molecular cloud, so that these data can be used to measure the demographics, life cycle, and physical state of molecular clouds across the population of galaxies where the majority of stars form at z = 0. This paper describes the scientific motivation and background for the survey, sample selection, global properties of the targets, Atacama Large Millimeter/submillimeter Array (ALMA) observations, and characteristics of the delivered data and derived data products. As the ALMA sample serves as the parent sample for parallel surveys with MUSE on the Very Large Telescope, the Hubble Space Telescope, AstroSat, the Very Large Array, and other facilities, we include a detailed discussion of the sample selection. We detail the estimation of galaxy mass, size, star formation rate, CO luminosity, and other properties, compare estimates using different systems and provide best-estimate integrated measurements for each target. We also report the design and execution of the ALMA observations, which combine a Cycle 5 Large Program, a series of smaller programs, and archival observations. Finally, we present the first 1 '' resolution atlas of CO emission from nearby galaxies and describe the properties and contents of the first PHANGS-ALMA public data release.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".