PRECISE ESTIMATES OF THE SYNTHETIC SPECTRAL DISTANCES TO DA WHITE DWARFS
Bibliographic record
Abstract
Using recent photometric calibrations, we develop greatly improved distance estimates for DA white dwarfs using multi-band synthetic photometry based on spectroscopic temperatures and gravities. Very good correlations are shown to exist between our spectroscopically based photometric distance estimates and those derived from trigonometric parallaxes. We investigate the uncertainties involved in our distance estimates, as well as discuss the circumstances where such techniques are most likely to fail. We apply our techniques to the large sample of Sloan Digital Sky Survey DA white dwarfs where automated fitting of H I Balmer profiles yields spectrometric temperatures and gravities. We determine simple empirical corrections to these temperatures and gravities with respect to published slit spectroscopy. After applying these T eff-log g corrections as well as appropriate interstellar extinction corrections, where necessary, we derive spectroscopically based photometric distances for 7062 DA stars from this sample.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".