PHANGS-ML: Dissecting Multiphase Gas and Dust in Nearby Galaxies Using Machine Learning
Bibliographic record
Abstract
Abstract The PHANGS survey uses Atacama Large Millimeter/submillimeter Array, Hubble Space Telescope, Very Large Telescope, and JWST to obtain an unprecedented high-resolution view of nearby galaxies, covering millions of spatially independent regions. The high dimensionality of such a diverse multiwavelength data set makes it challenging to identify new trends, particularly when they connect observables from different wavelengths. Here, we use unsupervised machine-learning algorithms to mine this information-rich data set to identify novel patterns. We focus on three of the PHANGS-JWST galaxies, for which we extract properties pertaining to their stellar populations; warm ionized and cold molecular gas; and polycyclic aromatic hydrocarbons (PAHs), as measured over 150 pc scale regions. We show that we can divide the regions into groups with distinct multiphase gas and PAH properties. In the process, we identify previously unknown galaxy-wide correlations between PAH band and optical line ratios and use our identified groups to interpret them. The correlations we measure can be naturally explained in a scenario where the PAHs and the ionized gas are exposed to different parts of the same radiation field that varies spatially across the galaxies. This scenario has several implications for nearby galaxies: (i) The uniform PAH ionized fraction on 150 pc scales suggests significant self-regulation in the interstellar medium, (ii) the PAH 11.3/7.7 μm band ratio may be used to constrain the shape of the non-ionizing far-ultraviolet to optical part of the radiation field, and (iii) the varying radiation field affects line ratios that are commonly used as PAH size diagnostics. Neglecting this effect leads to incorrect or biased PAH sizes.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".