The PHANGS-AstroSat Atlas of Nearby Star-forming Galaxies
Bibliographic record
Abstract
Abstract We present the Physics at High Angular resolution in Nearby GalaxieS (PHANGS)-AstroSat atlas, which contains UV imaging of 31 nearby star-forming galaxies captured by the Ultraviolet Imaging Telescope on the AstroSat satellite. The atlas provides a homogeneous data set of far-UV and near-UV maps of galaxies within a distance of 22 Mpc and a median angular resolution of 1.″4 (corresponding to a physical scale between 25 and 160 pc). After subtracting a uniform UV background and accounting for Milky Way extinction, we compare our estimated flux densities to GALEX observations, finding good agreement. We find candidate extended UV disks around the galaxies NGC 6744 and IC 5332. We present the first statistical measurements of the clumping of the UV emission and compare it to the clumping of molecular gas traced with the Atacama Large Millimeter/submillimeter Array (ALMA). We find that bars and spiral arms exhibit the highest degree of clumping, and the molecular gas is even more clumped than the far-UV (FUV) emission in galaxies. We investigate the variation of the ratio of observed FUV to H α in different galactic environments and kiloparsec-sized apertures. We report that ∼65% of the variation of the log 10 (FUV/H α ) can be described through a combination of dust attenuation with star formation history parameters. The PHANGS-AstroSat atlas enhances the multiwavelength coverage of our sample, offering a detailed perspective on star formation. When integrated with PHANGS data sets from ALMA, the Very Large Telescope-MUSE, the Hubble Space Telescope, and JWST, it develops our comprehensive understanding of attenuation curves and dust attenuation in star-forming galaxies.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".