The PHANGS-MUSE survey
Bibliographic record
Abstract
We present the PHANGS-MUSE survey, a programme that uses the MUSE integral field spectrograph at the ESO VLT to map 19 massive (9.4 < log(M⋆/M⊙)< 11.0) nearby (D ≲ 20 Mpc) star-forming disc galaxies. The survey consists of 168 MUSE pointings (1′ by 1′ each) and a total of nearly 15 × 106spectra, covering ∼1.5 × 106independent spectra. PHANGS-MUSE provides the first integral field spectrograph view of star formation across different local environments (including galaxy centres, bars, and spiral arms) in external galaxies at a median resolution of 50 pc, better than the mean inter-cloud distance in the ionised interstellar medium. This ‘cloud-scale’ resolution allows detailed demographics and characterisations of H IIregions and other ionised nebulae. PHANGS-MUSE further delivers a unique view on the associated gas and stellar kinematics and provides constraints on the star-formation history. The PHANGS-MUSE survey is complemented by dedicated ALMA CO(2–1) and multi-band HST observations, therefore allowing us to probe the key stages of the star-formation process from molecular clouds to H IIregions and star clusters. This paper describes the scientific motivation, sample selection, observational strategy, data reduction, and analysis process of the PHANGS-MUSE survey. We present our bespoke automated data-reduction framework, which is built on the reduction recipes provided by ESO but additionally allows for mosaicking and homogenisation of the point spread function. We further present a detailed quality assessment and a brief illustration of the potential scientific applications of the large set of PHANGS-MUSE data products generated by our data analysis framework. The data cubes and analysis data products described in this paper represent the basis for the first PHANGS-MUSE public data release and are available in the ESO archive and via the Canadian Astronomy Data Centre.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".