Self‐Correlation Analysis of the Photometric Variability of T Tauri Stars
Bibliographic record
Abstract
T Tauri stars are variable stars that are in an early phase of evolution, in which accretion and contraction to the main sequence are still taking place. Their photometric variability is complex; it takes place on a variety of timescales, due to a variety of physical processes. Periodic variability occurs due to rotation and the presence of cool or hot spots on the star. It may also occur due to periodic obscuration of the star by inhomogeneities in the still present accretion disk. But the periodicity may be masked by other forms of variability, or by time variation in the cool or hot spots, or the obscuring inhomogeneities. For other types of variable stars, self‐correlation has proven to be a useful adjunct to Fourier analysis for studying semiregular variability; it determines the cycle‐to‐cycle behavior of the star, averaged over all the data. We have therefore used it to investigate the photometric variability of about 30 T Tauri stars using existing data. It has provided useful information about periods and their coherence, about the amplitude of the periodic variation, or its upper limit, and about the "profile" of the amplitude‐timescale behavior. In most cases, it has confirmed periods previously determined by Fourier analysis, but in some cases it has suggested that the previously determined period is spurious.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 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".