Efficient identification of exoplanetary transit candidates from SuperWASP light curves
Bibliographic record
Abstract
Transiting extrasolar planets constitute only a small fraction of the range of stellar systems found to display periodic, shallow dimmings in wide-field surveys employing small-aperture camera arrays. Here we present an efficient selection strategy for follow-up observations, derived from analysis of the light curves of a sample of 67 SuperWASP targets that passed the selection tests we used in earlier papers, but which have subsequently been identified either as planet hosts or as astrophysical false positives. We determine the system parameters using Markov-chain Monte Carlo analysis of the SuperWASP light curves. We use a constrained optimization of 2 combined with a Bayesian prior based on the main-sequence mass and radius expected from the Two Micron All Sky Survey J -H colour. The Bayesian nature of the analysis allows us to quantify both the departure of the host star from the main-sequence mass-radius relation and the probability that the companion radius is less than 1.5 Jupiter radii. When augmented by direct light-curve analyses that detect binaries with unequal primary and secondary eclipses, and objects with aperture blends that are resolved by SuperWASP, we find that only 13 of the original 67 stars, including the three known planets in the sample, would qualify for follow-up. This suggests that planet discovery 'hit rates' better than
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".