SuperWASP-North extrasolar planet candidates between 3 \n < RA < 6
Bibliographic record
Abstract
The Wide Angle Search for Planets (WASP) photometrically surveys a large number of nearby stars to uncover candidate extrasolar planet systems by virtue of small-amplitude light curve dips on a ≲5-d time-scale typical of the ‘Hot-Jupiters’. Observations with the SuperWASP-North instrument between 2004 April and September produced a rich photometric data set of some 1.3 × 109 data points from 6.7 million stars. Our custom-built data acquisition and processing system produces ∼0.02 mag photometric precision at V= 13.\n\nWe present the transit candidates in the 03h–06h RA range. Out of 141 895 light curves with sufficient sampling to provide adequate coverage, 2688 show statistically significant transit-like periodicities. Out of these, 44 pass a visual inspection of the light curve, of which 24 are removed through a set of cuts on the statistical significance of artefacts. All but four of the remaining 20 objects are removed when prior information at higher spatial resolution from existing catalogues is taken into account. Of the four candidates remaining, one is considered a good candidate for follow-up observations with three further second-priority targets. We provide detailed information on these candidates, as well as a selection of the false-positives and astrophysical false-alarms that were eliminated, and discuss briefly the impact of sampling on our results.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".