INTEGRATED STELLAR POPULATIONS: CONFRONTING PHOTOMETRY WITH SPECTROSCOPY
Bibliographic record
Abstract
We investigate the ability of spectroscopic techniques to yield realistic star formation histories (SFHs) for the bulges of spiral galaxies based on a comparison with their observed broadband colors. Full spectrum fitting to optical spectra indicates that recent (within ∼1 Gyr) star formation activity can contribute significantly to the V -band flux, whilst accounting for only a minor fraction of the stellar mass budget which is made up primarily of old stars. Furthermore, recent implementations of stellar population (SP) models reveal that the inclusion of a more complete treatment of the thermally pulsating asymptotic giant branch (TP-AGB) phase to SP models greatly increases the NIR flux for SPs of ages 0.2–2 Gyr. Comparing the optical–NIR colors predicted from population synthesis fitting, using models which do not include all stages of the TP-AGB phase, to the observed colors reveals that observed optical–NIR colors are too red compared to the model predictions. However, when a 1 Gyr SP from models including a full treatment of the TP-AGB phase is used, the observed and predicted colors are in good agreement. This has strong implications for the interpretation of stellar populations, dust content, and SFHs derived from colors alone.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".