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 × 10 6 spectra, covering ∼1.5 × 10 6 independent 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 II regions 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 II regions 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".