The Multiband Imaging Survey for High-alpha PlanetS (MISHAPS). I. Preliminary Constraints on the Occurrence Rate of Hot Jupiters in 47 Tucanae
Bibliographic record
Abstract
Abstract The first generation of transiting planet searches in globular clusters yielded no detections, and in hindsight only placed occurrence rate limits slightly higher than the measured occurrence rate in the higher-metallicity Galactic thick disk. To improve these limits, we present the first results of a new wide-field search for transiting hot Jupiters in the globular cluster 47 Tucanae (47 Tuc). We have observed 47 Tuc as part of the Multiband Imaging Survey for High-Alpha PlanetS. Using 24 partial and full nights of observations taken with the Dark Energy Camera on the 4 m Blanco telescope at Cerro-Tololo Interamerican Observatory, we perform a search on 19,930 stars in the outer regions of the cluster. Though we find no clear planet detections, by combining our result with the upper limit enabled by R. L. Gilliland et al.’s Hubble search for planets around an independent sample of 34,091 stars in the inner cluster, we place the strongest limit to date on hot Jupiters with periods of 0.8 ≤ P ≤ 8.3 days and 0.5R Jup ≤ R P ≤ 2.0R Jup of f HJ < 0.11%, a factor of ∼4 below the occurrence rate in the Kepler field. Our search found 35 transiting planet candidates, though we are ultimately able to rule out each without follow-up observations. We also found four eclipsing binaries (EBs), including three previously uncataloged detached EB stars.
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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 | 0.001 | 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) | 0.001 | 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".