Using adaptive-optics assisted MUSE observations to measure galaxy distances with the Planetary Nebula luminosity function
Bibliographic record
Abstract
Abstract Thanks to its characteristic bright cut-off, the planetary nebulae luminosity function (PNLF) has now become a well-established extragalactic distance indicator that is in principle applicable to all types of galaxies. Most recently, several studies have demonstrated how the use of integral-field spectroscopy can lead to even more precise PNLF measurements, in particular as it allows to probe the central regions of galaxies and obtain well-sampled PNLF distributions. In this respect, adaptive optics (AO) is expected to further increase the scope and reach of PNLF measurements, as it should allow for the detection of even more and more distant PNe. This proceeding presents first results of the investigation of the MUSE-AO performance in relation to the detection of PNe in external galaxies, based on all galaxies with wide-field mode AO observations in the ESO archive.
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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 | 0.001 |
| 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".