An Algorithm to Detect Blends with Eclipsing Binaries in Planet Transit Searches
Bibliographic record
Abstract
We present an algorithm that can detect blends of bright stars with fainter, unassociated eclipsing binaries. Such systems contaminate searches for transiting planets, particularly in crowded fields where blends are common. Spectroscopic follow-up observations on large-aperture telescopes have been used to reject these blends, but the results are not always conclusive. Our approach exploits the fact that a blend with an eclipsing binary changes its shape during eclipse. We analyze original imaging data from the Optical Gravitational Lensing Experiment (OGLE), which were used to discover planet transit candidates. Adopting a technique developed in weak gravitational lensing to carefully correct for the point-spread function, which varies both with time and across the field, we demonstrate that ellipticities can be measured with great accuracy using an ensemble of images. Applied to OGLE-TR-3 and OGLE-TR-56, two transit systems, we show that both systems are blended with fainter stars, as are most other stars in the OGLE fields. Moreover, while we do not detect shape change when TR-56 undergoes transits, TR-3 exhibits a significant shape change during eclipses. We therefore conclude that TR-3 is indeed a blend with an eclipsing binary, as has been suggested from other lines of evidence. The probability that its shape change is caused by residual systematics is found to be less than 0.6%. Our technique incurs no follow-up cost and requires little human interaction. As such, it could become part of the data pipeline for any planetary transit search to minimize contamination by blends. We briefly discuss its relevance for the Kepler mission and for binary star detection.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".