Why Estimating <i>η</i> <sub>⊕</sub> is Difficult: A Kepler-Centric Perspective
Bibliographic record
Abstract
Abstract η ⊕ , the occurrence rate of rocky habitable zone exoplanets orbiting Sun-like stars, is of great interest to both the astronomical community and the general public. The Kepler space telescope has made it possible to estimate η ⊕ , but estimates by different groups vary by more than an order of magnitude. We identify several causes for this range of estimates. We first review why, despite being designed to estimate η ⊕ , Kepler’s observations are not sufficient for a high-confidence estimate, due to Kepler’s detection limit coinciding with the η ⊕ regime. This results in a need to infer η ⊕ , for example extrapolating from a regime of non-habitable zone, non-rocky exoplanets. We examine two broad classes of causes that can account for the large discrepancy in η ⊕ found in the literature: (a) differences in definitions and input data between studies, and (b) fundamental limits in Kepler data that lead to large uncertainties and poor accuracy. We highlight the risk of large biases when using extrapolation to describe small exoplanet populations in the habitable zone. We discuss how η ⊕ estimates based on Kepler data can be improved, such as reprocessing Kepler data for more complete, higher-reliability detections and better exoplanet catalog characterization. We briefly survey upcoming space telescopes capable of measuring η ⊕ , and how they can be used to supplement Kepler data.
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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 | 0.021 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".