Multiband analysis of the O’Connell effect in 14 eclipsing binaries
Bibliographic record
Abstract
Context. The O’Connell effect is a phenomenon in eclipsing binary (EB) systems that consists of unequal maxima in a light curve when out of eclipse. Despite being known for decades and with several theories proposed over the years, this effect is still not fully understood. Aims. Our goal is to find different O’Connell effect properties using a multiband approach, compare them with each other, and find correlations between these properties and the physical parameters of the systems. Methods. We present the analysis of 14 new EBs that show the O’Connell effect using multiband data extracted from the Asteroid Terrestrial-impact Last Alert System (ATLAS) and Zwicky Transient Facility (ZTF) all-sky surveys. We measured the difference in maximum amplitudes (δm) alongside different light curve features in different passbands via a new modeling process that uses Gaussian fits. We created a brand-new phenomenological model for O’Connell effect systems whose analysis had previously been hampered by overfitting. Results. Although the magnitude of the O’Connell effect seems to be more pronounced at shorter effective wavelengths, supporting the idea that cool starspots cause the effect, a conclusive correlation is not found. On the other hand, we do find strong correlations between the magnitude of the effect and both the temperature and the period, both of which are inconsistent with a previous study. We also find that in systems that show both positive and negative O’Connell effects, there are different correlations with the aforementioned parameters. Conclusions. We conclude that, even though starspots may be one cause of the O’Connell effect, it is likely a multipronged phenomenon; for instance, the physical interaction between the components of close binary stars may be another important factor.
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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 | 0.000 | 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) | 0.001 | 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".