The Frequency of Large‐Radius Hot and Very Hot Jupiters in ω Centauri
Bibliographic record
Abstract
We present the results of a deep, wide-field search for transiting hot Jupiter (HJ) planets in the globular cluster ω Centauri. As a result of a 25 night observing run with the ANU 40 inch (1 m) telescope at Siding Spring Observatory, a total of 109,726 stellar time series composed of 787 independent data points were produced with differential photometry in a 52' × 52' (0.75 deg 2 ) field centered on the cluster core, but extending well beyond. Taking into account the size of transit signals as a function of stellar radius, 45,406 stars have suitable photometric accuracy (≤0.045 mag to V = 19.5) to search for transits. Of this sample, 31,000 stars are expected to be main-sequence cluster members. All stars, both cluster and foreground, were subjected to a rigorous search for transit signatures; none were found. Extensive Monte Carlo simulations based on our actual data set allow us to determine the sensitivity of our survey to planets with radii ~1.5 R Jup and thus place statistical upper limits on their occurrence frequency F . Smaller planets are undetectable in our data. At 95% confidence, the frequency of very hot Jupiters (VHJs) with periods P satisfying 1 day < P < 3 days can be no more than F VHJ < 1/1040 in ω Cen. For HJs and VHJs distributed uniformly over the orbital period range 1 day < P < 5 days, F VHJ+HJ < 1/600. Our limits on large, short-period planets are comparable to those recently reported for other Galactic fields, despite being derived with less telescope time.
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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 | 0.001 |
| 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".