Comparing the Locations of Supernovae to CO (2–1) Emission in Their Host Galaxies
Bibliographic record
Abstract
Abstract We measure the molecular gas environment near recent (<100 yr old) supernovae (SNe) using ∼1″ or ≤150 pc resolution CO (2–1) maps from the PHANGS–Atacama Large Millimeter/submillimeter Array (ALMA) survey of nearby star-forming galaxies. This is arguably the first such study to approach the scales of individual massive molecular clouds (M mol ≳ 105.3 M ⊙). Using the Open Supernova Catalog, we identify 63 SNe within the PHANGS–ALMA footprint. We detect CO (2–1) emission near ∼60% of the sample at 150 pc resolution, compared to ∼35% of map pixels with CO (2–1) emission, and up to ∼95% of the SNe at 1 kpc resolution, compared to ∼80% of map pixels with CO (2–1) emission. We expect the ∼60% of SNe within the same 150 pc beam, as a giant molecular cloud will likely interact with these clouds in the future, consistent with the observation of widespread SN–molecular gas interaction in the Milky Way, while the other ∼40% of SNe without strong CO (2–1) detections will deposit their energy in the diffuse interstellar medium, perhaps helping drive large-scale turbulence or galactic outflows. Broken down by type, we detect CO (2–1) emission at the sites of ∼85% of our 9 stripped-envelope SNe (SESNe), ∼40% of our 34 Type II SNe, and ∼35% of our 13 Type Ia SNe, indicating that SESNe are most closely associated with the brightest CO (2–1) emitting regions in our sample. Our results confirm that SN explosions are not restricted to only the densest gas, and instead exert feedback across a wide range of molecular gas densities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".