Planetary nebulae as tracers of stellar population properties: a pilot study with MUSE
Bibliographic record
Abstract
ABSTRACT Planetary nebulae (PNe) are the only single stars in galaxies outside the Local Group that can be used as kinematic tracers of the diffuse light in the extended halo. Analysing their luminosity-specific number density across galaxies of different morphologies has also shown hints that they may be used as tracers of the age and metallicity of stellar populations. A proper understanding of this relation has been hindered by the fact that simultaneously detecting PNe and accurately measuring stellar properties is extremely difficult using classical narrow-band imaging methods, which cannot detect PNe in the bright centres of galaxies. In this work, we use integral-field spectroscopy to overcome this challenge, analysing the inner regions of a sample of 10 early-type galaxies from the Extended Planetary Nebulae Survey (ePN.S) for which archival MUSE data were available. With the Diffuse Emission-Line Filter (DELF) technique, we automate the detection of PNe, and perform spectral fitting on the diffuse light to infer kinematics and stellar population parameters. We compare the PN number density profile and its associated $\alpha$-parameter with multiple properties of the host galaxies. We find that our sample follows the previously observationally constrained correlation with the metallicity of the host galaxy. We find a weak anticorrelation between the $\alpha$-parameter and the far-ultraviolet excess, highlighting the possible relation between the visibility lifetime of PNe on the spectral energy distribution of their host galaxies, with fewer PNe detected in association with stellar populations characterized by an ultraviolet excess.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".