BSN: The First Light Curve Analysis of the Total Eclipse Binary System EL Tuc
Bibliographic record
Abstract
Abstract We conducted the first light curve study of the binary star EL Tuc within the Binary Systems of South and North project’s framework. The photometric observations were made using standard multiband BVR c I c filters at an observatory in Argentina. We presented a new ephemeris for EL Tuc and a linear fit to the O – C diagram, utilizing our extracted times of minima and additional literature. We employed the PHysics Of Eclipsing BinariEs Python code and the Markov chain Monte Carlo approach for the system’s light curve analysis. The target system’s light curve solution required a cold starspot on the hotter component. We conclude that EL Tuc is a total contact binary system with a low mass ratio of q = 0.172 ± 0.002, an orbital inclination of i = 83.°74 ± 0.°40, and a fillout factor of f = 53.7% ± 1.6%. We used the P–a relationship and the Gaia Data Release 3 parallax method to determine the absolute parameters of EL Tuc to compare the precision of our results. This system was classified as W-type based on the mass and effective temperature of the companion stars. The positions of the systems were depicted on the M–L, M–R, T–M, and q–L ratio diagrams. The relationship between the spectroscopic and photometric mass ratios of binaries was discussed.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".