A systematic study of variability among OB-stars based on HIPPARCOS photometry
Bibliographic record
Abstract
Context. Variability is a key factor for understanding the nature of the most massive stars, the OB stars. Such stars lie closest to the unstable upper limit of star formation.Aims. In terms of statistics, the data from the HIPPARCOS satellite are unique because of time coverage and uniformity. They are ideal to study variability in this large, uniform sample of OB stars. Methods. We used statistical techniques to determine an independant threshold of variability corresponding to our sample of OB stars, and then applied an automatic algorithm to search for periods in the data of stars that are located above this threshold. We separated the sample stars into 4 main categories of variability: 3 intrinsic and 1 extrinsic. The intrinsic categories are: OB main sequence stars (~2/3 of the sample), OBe stars (~10%) and OB Supergiant stars (~1/4).The extrinsic category refers to eclipsing binaries. Results. We classified about 30% of the whole sample as variable, although the fraction depends on magnitude level due to instrumental limitations. OBe stars tend to be much more variable (≈80%) than the average sample star, while OBMS stars are below average and OBSG stars are average. Types of variables include α Cyg, β Cep, slowly pulsating stars and other types from the general catalog of variable stars. As for eclipsing binaries, there are relatively more contact than detached systems among the OBMS and OBe stars, and about equal numbers among OBSG stars.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".