Stellar Populations and Kinematics in Spiral Galaxies
Bibliographic record
Abstract
Abstract We present a detailed study of the stellar populations (SPs) and kinematics of the bulge and inner disk regions of nearby spiral galaxies (Sa-Sd) based on deep long-slit Gemini/GMOS data. We find that the SPs of spiral galaxies are not well matched by single episodes of star formation; representative SPs must involve average SP values integrated over the star formation history (SFH) of the galaxy, such as those derived from the “full population synthesis” method used here. Our spiral bulges follow the same correlations of increasing light-weighted age and metallicity with central velocity dispersion as those of elliptical galaxies and early-type bulges found in other studies, but when SFHs more complex and realistic than a single burst are invoked, the trend with age is shallower and its scatter much reduced. In a mass-weighted context, all bulges are predominantly composed of old and metal-rich SPs. Bulge formation appears to be dominated by early processes that are common to all spheroids, whether they currently reside in disks or not. While monolithic collapse cannot be ruled out in some cases, merging must be invoked to explain the SP gradients in most bulges. Further bulge growth via secular processes, or “rejuvenated” star formation, generally contributes minimally to the stellar mass budget. We also demonstrate how the combination of our full population synthesis modeling of high-quality optical spectra of integrated SPs along with optical-NIR broad-band imaging can single out potential model weaknesses and help determine the reliability of the inferred SFHs.
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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 | 0.000 | 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".