The stellar spectral features of nearby galaxies in the near infrared: tracers of thermally pulsing asymptotic giant branch stars?
Bibliographic record
Abstract
We analyse the stellar absorption features in high signal-to-noise ratio (S/N) near-infrared (NIR) spectra of the nuclear region of 12 nearby galaxies, mostly spirals. The features detected in some or all of the galaxies in this sample are the TiO (0.843 and 0.886 μm), VO (1.048 μm), CN (1.1 and 1.4 μm), H2O (1.4 and 1.9 μm) and CO (1.6 and 2.3 μm) bands. The C2 (1.17 and 1.76 μm) bands are generally weak or absent, although C2 (1.76 μm) may be weakly present in the mean galaxy spectrum. A deep feature near 0.93 μm, likely caused by CN, TiO and/or ZrO, is also detected in all objects. Fitting a combination of stellar spectra to the mean spectrum shows that the absorption features are produced by evolved stars: cool giants and supergiant stars in the early- or thermally pulsing asymptotic giant branch (E-AGB or TP-AGB) phases. The high luminosity of TP-AGB stars, and the appearance of VO and ZrO features in the data, suggest that TP-AGB stars dominate these spectral features. However, a contribution from other evolved stars is also likely. Comparison with evolutionary population synthesis models shows that models based on empirical libraries that predict relatively strong NIR features provide a more accurate description of the data. However, none of the models tested accurately reproduces all of the features observed in the spectra. To do so, the models will need to not only improve the treatment of TP-AGB stars, but also include good quality spectra of red giant and E-AGB stars. The uninterrupted wavelength coverage, high S/N and quantity of features we present here will provide a benchmark for the next generation of models aiming to explain and predict the NIR properties of 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 | 0.001 | 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".