Empirical Predictions for the Period Distribution of Planets to Be Discovered by the <i>Transiting Exoplanet Survey Satellite</i>
Bibliographic record
Abstract
Launched in 2018 April, NASA's Transiting Exoplanet Survey Satellite ( TESS ) has been performing a wide-field survey for exoplanets orbiting bright stars with a goal of producing a rich database for follow-on studies. Here we present estimates of the detected exoplanet orbital periods in the 2 minute cadence mode during the TESS mission. For a two-transit detection criterion, the expected mean value of the most frequently detected orbital period is 5.01 days, with the most frequently detected range of 2.12–11.82 days in the region with observation of 27 days. Near the poles where the observational duration is 351 days, the expected mean orbital period is 10.93 days, with the most frequently detected range being from 3.35 to 35.65 days. For one transit, the most frequently detected orbital period is 8.17 days in the region with observation of 27 days and 11.25 days in the region near the poles. For the entire TESS mission containing several sectors, we estimate that the mean value of orbital period is 8.47 days for two-transit detection criterion and 10.09 days for one-transit detection criterion. If TESS yields a planet population substantially different from what is predicted here, the underlying planet occurrence rates are likely different between the stellar sample probed by TESS and that by Kepler .
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".