BSN-I: the first in-depth photometric study of seven total-eclipse contact binary systems
Bibliographic record
Abstract
ABSTRACT This is the first in-depth study of seven total-eclipse W Ursae Majoris-type contact binary systems using photometric light curves. The ground-based observations were conducted with four observatories in the Northern and Southern hemispheres. We also used the Transiting Exoplanet Survey Satellite for four target systems. We presented the analysis of orbital period variations of six systems and found that they display parabolic variations. The material transfer rates between the stars of the systems were calculated. Also, the results show that four systems have a long-term increase, while two have a long-term decrease in their orbital periods. We analysed light curves using the PHysics Of Eclipsing BinariEs python code and the Markov chain Monte Carlo algorithm to estimate different parameters of target systems and their uncertainties. Six of the target systems required the addition of a cold or hot star-spot. We estimated absolute parameters using the empirical relationship between the orbital period and the semimajor axis ($P\!\!-\!\!a$). According to each component’s effective temperature and mass, it was recognized that the studied systems are W subtype. We examined the dynamic stability of two targets, which were low mass ratio contact binary systems. We also showed the evolution of stars in the $M\!\!-\!\!R$ and $M\!\!-\!\!L$ diagrams. Finally, we showed that the hotter stars in contact systems have a temperature difference of less than ${\approx} 400$ K compared to the Gaia Data Release 3 temperature report.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".