Effective temperatures of classical Cepheids from line-depth ratios in the <i>H</i>-band
Bibliographic record
Abstract
ABSTRACT The technique of line depth ratios (LDRs) is one of the methods to determine the effective temperature of a star. They are crucial in the spectroscopic studies of variable stars like Cepheids since no simultaneous photometry is usually available. A good number of LDR-temperature relations are already available in the optical domain; here we want to expand the number of relations available in the near-infrared (NIR) in order to fully exploit the capabilities of current and upcoming NIR spectrographs. We used 115 simultaneous spectroscopic observations in the optical and the NIR for six Cepheids and optical LDRs to find new pairs of lines sensitive to temperature and to calibrate LDR-temperature relations in the NIR spectral range. We have derived 87 temperature calibrations valid in the [4800–6500] K range of temperatures. The typical uncertainty for a given relation is 60–70 K, and combining many of them provides a final precision within 30–50 K. We found a discrepancy between temperatures derived from optical or NIR LDR for pulsations phases close to ϕ ≈0.0 and we discuss the possible causes for these differences. LDRs in the NIR will allow us to spectroscopically investigate highly reddened Cepheids in the Galactic centre or in the far side of the disc.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".