Bioverse: Assessing the Ability of Direct Imaging Surveys to Empirically Constrain the Habitable Zone via Trends in Albedo
Bibliographic record
Abstract
Abstract Will future direct imaging missions such as NASA’s upcoming Habitable Worlds Observatory (HWO) be able to understand Earth-sized planets as a population? In this study, we simulate the ability of space-based coronagraphy missions to uncover trends in planetary albedo as a function of instellation, and potentially constrain the boundaries of the habitable zone (HZ). We adapt the Bioverse statistical comparative planetology framework to simulate the scientific output of possible designs for HWO. With this tool, we generate a synthetic planetary population with injected population-level trends in albedo and simulate the observability of planets. We then determine the statistical power to which these trends can be recovered as a function of the strength of the injected trend and the sample size of Earth-sized planets in the habitable zone (exoEarths). The strongest trends in albedo require a sample size of roughly 25–30 exoEarths to recover with high confidence. However, for weaker albedo trends, the required number of planets increases rapidly. If a mission is designed to meet the Decadal Survey’s requirement of 25 exoEarths, it would be able to recover very strong trends in albedo associated with the HZ, but would struggle to confidently detect weaker trends. We explore multiple strategies to increase one’s ability to recover weak trends, such as reducing the uncertainties in observables, incorporating additional observables such as planet colors, and obtaining direct constraints on planetary albedo from full spectral retrievals.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".