Quick-MESS: A Fast Statistical Tool for Exoplanet Imaging Surveys
Bibliographic record
Abstract
Several tools have been developed in the past few years for the statistical analysis of the exoplanet search surveys, mostly using a combination of Monte Carlo simulations or a Bayesian approach. Here we present Quick-MESS, a grid-based, non-Monte Carlo tool aimed to perform statistical analyses on results from direct imaging surveys, as well as help with the planning of these surveys. Quick-MESS uses the (expected) contrast curves for direct imaging surveys to assess for each target the probability that a planet of a given mass and semimajor axis can be detected. By using a grid-based approach, Quick-MESS is typically more than an order of magnitude faster than tools based on Monte Carlo sampling of the planet distribution. In addition, Quick-MESS is extremely flexible, enabling the study of a large range of parameter space for the mass and semimajor axes distributions without the need of resimulating the planet distribution. In order to show examples of the capabilities of Quick-MESS, we present the analysis of the Gemini Deep Planet Survey and the predictions for upcoming surveys with extreme-AO instruments.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.014 |
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".