Toward an Empirical Determination of the ZZ Ceti Instability Strip
Bibliographic record
Abstract
We present atmospheric parameters for a large sample of DA white dwarfs that are known to be photometrically constant. For each star, we determine the effective temperature and surface gravity by comparing high signal-to-noise ratio optical spectra to the predictions of detailed model atmosphere calculations. We also report the successful prediction and detection of photometric variability in G232-38 based on similar T eff and log g determinations. The atmospheric parameters derived for this sample of constant stars, as well as those for the known sample of bright ZZ Ceti stars (now boosted to a total of 39), have been obtained in a highly homogeneous way. We combine them to study the empirical red and blue edges, as well as the purity of the ZZ Ceti instability strip. We find that the red edge is rather well constrained, whereas there exists a rather large range of possibilities for the slope of the blue edge. Furthermore, the ZZ Ceti instability strip that results from our analysis contains no nonvariable white dwarfs. Our sample of constant stars is part of a much broader spectroscopic survey of bright ( V < 17) DA white dwarfs, which we have recently undertaken. We also present here some preliminary results of this survey. Finally, we revisit the analysis by Mukadam et al. of the variable and nonvariable DA stars uncovered as part of the Sloan Digital Sky Survey. Their erroneous conclusion of an instability strip containing several nonvariable stars is traced back to the low signal-to-noise ratio spectroscopic observations used in that survey.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".