Versatile light curve templates of Cepheids
Bibliographic record
Abstract
Context. To use the period–luminosity relations of Cepheids to derive distances of extragalactic Cepheids, it is crucial to have reliable estimations of the luminosity of such stars. However, the light curves (LCs) of extragalactic Cepheids are sparsely sampled, which makes the interpolation difficult. Aims. Our purpose is to create general templates of Cepheid LCs that can be efficiently applied with only a few parameters, while ensuring they are reliable in terms of resulting mean magnitudes. We have also aimed to provide adaptable templates for the large variety of photometric bands to enlarge their possible applications. Methods. We performed a principal component analysis (PCA) on LCs in different bands from the optical to the mid-infrared (MIR) fitted by atmosphere models along the pulsation cycle for 75 galactic Cepheids. All the photometric bands were treated together to incorporate the results of the effective temperature variation. To link the different shapes of LCs, we also derived the relations between the period and the coefficients associated to the PCA. Results. We present our templates and a further analysis to validate their reliability. We also offer an example of their application to one Cepheid in the galaxy NGC 5584. We find a good agreement with previous estimations of mean magnitudes in the HST filters. We show examples of a template fitting for Cepheids in various environments to demonstrate the variety of applications of these templates. Conclusions. The templates we propose here can be used in a number of different situations (good phase coverage in many filters, poor phase coverage in one specific filter, but good phase coverage in some others, or noisy and sparse data), thereby mobilising the adapted fitting strategies. Moreover, the broad adaptability of this LC prediction tool is provided by the multiple photometric bands in which they can be inferred.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.002 |
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".