The PHANGS-HST-H<i>α</i> Survey: Warm Ionized Gas Physics at High Angular Resolution in Nearby Galaxies with the Hubble Space Telescope
Bibliographic record
Abstract
Abstract The PHANGS project is assembling a comprehensive, multiwavelength data set of nearby (∼5–20 Mpc), massive star-forming galaxies to enable multiphase, multiscale investigations into the processes that drive star formation and galaxy evolution. To date, large survey programs have provided molecular gas (CO) cubes with the Atacama Large Millimeter/submillimeter Array, optical integral field unit (IFU) spectroscopy with the Very Large Telescope/Multi-Unit Spectroscopic Explorer (MUSE), high-resolution near-ultraviolet–optical imaging in five broadband filters with Hubble Space Telescope (HST), and infrared imaging in NIRCAM+MIRI filters with JWST. Here we present PHANGS-HST-H α , which has obtained high-resolution (∼2–10 pc), narrowband imaging in the F658N or F657N filters with the HST/WFC3 camera of the warm ionized gas in the first 19 nearby galaxies observed in common by all four of the PHANGS large programs. We summarize our data reduction process, with a detailed discussion of the production of flux-calibrated, Milky Way extinction-corrected, continuum-subtracted H α maps. PHANGS-MUSE IFU spectroscopy data are used to background-subtract the HST-H α maps and to determine the [N ii ] correction factors for each galaxy. We describe our public data products (the data released as part of this work include the reduced drizzled narrowband images and the flux-calibrated, continuum-subtracted H α maps for each galaxy; these images are available for download via MAST at https://archive.stsci.edu/hlsp/phangs.html , as well as at the Canadian Astronomy Data Centre as part of the PHANGS archive at https://www.canfar.net/storage/vault/list/phangs/RELEASES ) and highlight a few key science cases enabled by the PHANGS-HST-H α observations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".