The Direct Imaging Search for Earth 2.0: Quantifying Biases and Planetary False Positives
Bibliographic record
Abstract
Abstract Direct imaging is likely the best way to characterize the atmospheres of Earth-sized exoplanets in the habitable zone of Sun-like stars. Previously, Stark et al. estimated the Earth twin yield of future direct imaging missions, such as LUVOIR and HabEx. We extend this analysis to other types of planets, which will act as false positives for Earth twins. We define an Earth twin as any exoplanet within half an e-folding of 1 au in semimajor axis and 1 in planetary radius, orbiting a G-dwarf. Using Monte Carlo analyses, we quantify the biases and planetary false-positive rates of Earth searches. That is, given a pale dot at the correct projected separation and brightness to be a candidate Earth, what are the odds that it is, in fact, an Earth twin? Our notional telescope has a diameter of 10 m, an inner working angle of 3λ/D, and an outer working angle of 10λ/D (62 mas and 206 mas at 1.0 μm). With no precursor knowledge and one visit per star, 77% of detected candidate Earths are actually un-Earths; their mean radius is 2.3 , a sub-Neptune. The odds improve if we image every planet at its optimal orbital phase, either by relying on precursor knowledge, or by performing multi-epoch direct imaging. In such a targeted search, 47% of detected Earth twin candidates are false positives, and they have a mean radius of 1.7 . The false-positive rate is insensitive to stellar spectral type and the assumption of circular orbits.
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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 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".