The PHANGS–JWST Treasury Survey: Star Formation, Feedback, and Dust Physics at High Angular Resolution in Nearby GalaxieS
Bibliographic record
Abstract
Abstract The PHANGS collaboration has been building a reference data set for the multiscale, multiphase study of star formation and the interstellar medium (ISM) in nearby galaxies. With the successful launch and commissioning of JWST, we can now obtain high-resolution infrared imaging to probe the youngest stellar populations and dust emission on the scales of star clusters and molecular clouds (∼5–50 pc). In Cycle 1, PHANGS is conducting an eight-band imaging survey from 2 to 21 μm of 19 nearby spiral galaxies. Optical integral field spectroscopy, CO(2–1) mapping, and UV-optical imaging for all 19 galaxies have been obtained through large programs with ALMA, VLT-MUSE, and Hubble. PHANGS–JWST enables a full inventory of star formation, accurate measurement of the mass and age of star clusters, identification of the youngest embedded stellar populations, and characterization of the physical state of small dust grains. When combined with Hubble catalogs of ∼10,000 star clusters, MUSE spectroscopic mapping of ∼20,000 H ii regions, and ∼12,000 ALMA-identified molecular clouds, it becomes possible to measure the timescales and efficiencies of the earliest phases of star formation and feedback, build an empirical model of the dependence of small dust grain properties on local ISM conditions, and test our understanding of how dust-reprocessed starlight traces star formation activity, all across a diversity of galactic environments. Here we describe the PHANGS–JWST Treasury survey, present the remarkable imaging obtained in the first few months of science operations, and provide context for the initial results presented in the first series of PHANGS–JWST publications.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".