Azimuthal Variations of Stellar Populations in Barred Galaxies
Bibliographic record
Abstract
Bars are expected to impact the distribution of stellar populations both during bar formation, as they rearrange stars into new orbits, and afterwards, due to the redistribution of star-formation-fuelling gas and transfer of angular momentum. We study the impact of stellar bars on the azimuthal variation of stellar population age, metallicity and mass surface density in $\sim1\,000$ nearby barred galaxies from the SDSS-IV/MaNGA survey. Bars have higher stellar mass density ($0.113^{+0.065}_{-0.067}$ dex) and are more metal-rich ($0.028^{+0.033}_{-0.040}$ dex) than the discs at the same radii. Stellar ages show a variety of bar to inter-bar contrasts with no consistent trend. The difference in metallicity increases with total stellar mass of the galaxy and distance below the star-forming main sequence. We discuss a combination of potentially responsible processes including kinematic separation, more extended star formation histories and more efficient recycling in bars and at bar-spiral arm connections. Additionally, we observe an offset (10°-40°) of the peak metallicity to the bar major axis in star-forming bars in low-mass galaxies, and more metal-rich regions outside the ends of the bar in long bars and quenched galaxies. Furthermore, there is a subtle trend of lower metallicities on the leading side of spiral arms compared to the trailing side. Finally, we report a spiral arm surface density feature, which could point towards a dominant bar-spiral connection and pitch angle of $α\sim 25$°. We interpret these features in the context of bar formation and the impact of large-scale gas flows associated with their presence.
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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 | 0.001 |
| 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) | 0.001 | 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".