Planetary nebulae as tracers of stellar population properties: unlocking their potential with integral-field spectroscopy
Bibliographic record
Abstract
Planetary nebulae (PNe) are essential tracers of the kinematics of the diffuse halo and intracluster light where stellar spectroscopy is unfeasible, due to their strong emission lines. However, that is not all they can reveal about the underlying stellar population. In recent years, it has also been found that PNe in the metal-poor halos of galaxies have different properties (specific frequency, luminosity function), than PNe in the more metal-rich galaxy centers. A more quantitative understanding of the role of age and metallicity in these relations would turn PNe into valuable stellar-population tracers. In order to do that, a full characterization of PNe in regions where the stellar light can also be analysed in detail is necessary. In this work, we make use of integral-field spectroscopic data covering the central regions of galaxies, which allow us to measure both stellar ages and metallicities as well as to detect PNe. This analysis is fundamental to calibrate PNe as stellar population tracers and to push our understanding of galaxy properties at unprecedented galactocentric distances.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".