BSN: Light Curve Modeling and Orbital Evolution of the Total-eclipse Contact Binary EZ Oct
Bibliographic record
Abstract
Abstract We present the first detailed multiband ( BVR c I c and TESS) photometric analysis of the short-period binary EZ Oct. This study combines ground-based observations conducted at a Southern Hemisphere observatory in Argentina with data from the TESS mission. Investigating the orbital period variations of EZ Oct reveals a steadily increasing period consistent with a quadratic trend. We present a new ephemeris and estimate the mass transfer rate as M ̇ = 1.353 × 1 0 − 8 M ⊙ yr −1 , indicating ongoing conservative mass transfer from the less massive to the more massive star. Light curve modeling was performed using the PHOEBE Python code in conjunction with the MCMC approach, and the inclusion of a cold starspot was required to achieve an adequate fit. Absolute parameters were estimated using Gaia DR3 parallax and astrophysical equations. Our analysis shows that EZ Oct is a total-eclipse contact binary with a mass ratio of 1.969, a fillout factor of 0.106, and an inclination of 82 . ° 13. Based on the stellar masses and temperatures of the components, the target system belongs to the W-subtype of contact binaries. The positions of the component stars were displayed on the mass—luminosity and mass—radius diagrams to illustrate their evolutionary status. Moreover, we investigated the relationship between orbital period and stellar luminosity in contact binary stars using a sample of 461 systems with P < 0.5 day. We highlight the position of EZ Oct in the mass ratio—inclination parameter space, showing that it lies within the densely populated region of contact binaries.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".