Planetary nebula luminosity function distances for 19 galaxies observed by PHANGS–MUSE
Bibliographic record
Abstract
ABSTRACT We provide new planetary nebula luminosity function (pnlf) distances to 19 nearby spiral galaxies that were observed with VLT/MUSE by the PHANGS collaboration. Emission line ratios are used to separate planetary nebulae (pne) from other bright $[\mathrm{O}\, {\small III}]$ emitting sources like compact supernovae remnants (snrs) or H ii regions. While many studies have used narrowband imaging for this purpose, the detailed spectral line information provided by integral field unit (ifu) spectroscopy grants a more robust way of categorizing different $[\mathrm{O}\, {\small III}]$ emitters. We investigate the effects of snr contamination on the pnlf and find that we would fail to classify all objects correctly, when limited to the same data narrowband imaging provides. However, the few misclassified objects usually do not fall on the bright end of the luminosity function, and only in three cases does the distance change by more than 1σ. We find generally good agreement with literature values from other methods. Using metallicity constraints that have also been derived from the same ifu data, we revisit the pnlf zero-point calibration. Over a range of 8.34 < 12 + log (O/H) < 8.59, our sample is consistent with a constant zero-point and yields a value of $M^{*} = -4.542^{+0.103}_{-0.059}\, \mathrm{mag}$, within 1σ of other literature values. MUSE pushes the limits of pnlf studies and makes galaxies beyond ${20}{\, \mathrm{Mpc}}$ accessible for this kind of analysis. This approach to the pnlf shows great promise for leveraging existing archival ifu data on nearby 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".