Carbon stars as standard candles – III. Un-binned maximum likelihood fitting and comparison with TRGB estimations
Bibliographic record
Abstract
ABSTRACT In the second paper of this series, we developed a new distance determination method using the median J magnitude of carbon-rich asymptotic giant branch stars (CS) as standard candles and the Magellanic Clouds as the fundamental calibrators. The J-band CS luminosity function was modelled using a modified Lorentzian distribution whose parameters were used to determine whether the Large or Small Magellanic cloud was the most suitable calibrator. In this third paper of the series, we expand our sample of galaxies and introduce a more robust method to determine the parameters of the Lorentzian model. The new fitting method uses an un-binned maximum likelihood estimator to determine the parameters of the Lorentzian model resulting in parameter errors that are significantly smaller compared to the second paper. We test our method in NGC 6822, IC 1613, NGC 3109, and WLM. We also estimate the distances to the same sample of galaxies via the tip of the red giant branch (TRGB) detection method. Our results from the CS measurements agree well with those obtained from the TRGB.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".