Bibliographic record
Abstract
Abstract We reexamine the expected yield of Gaia astrometric planet detections using updated models for giant-planet occurrence, the local stellar population, and Gaia’s demonstrated astrometric precision. Our analysis combines a semianalytic model that clarifies key scaling relations with more realistic Monte Carlo simulations. We predict 7500 ± 2100 planet discoveries in the 5 yr dataset (Data Release 4 (DR4)) and 120,000 ± 22,000 over the full 10 yr mission (Data Release 5 (DR5)), with the dominant error arising from uncertainties in giant-planet occurrence. We evaluate the sensitivity of these forecasts to the detection threshold and the desired precision for measurements of planet masses and orbital parameters. Roughly 1900 ± 540 planets in DR4 and 38,000 ± 7300 planets in DR5 should have masses and orbital periods determined to better than 20%. Most detections will be super-Jupiters (3–13 M J ) on 2–5 au orbits around GKM-type stars (0.4–1.3 M ⊙ ) within 500 pc. Unresolved binary stars will lead to spurious planet detections, but we estimate that genuine planets will outnumber them by a factor of 5 or more. An exception is planets around M dwarfs with a < 1 au, for which the false-positive rate is expected to be about 50%. To support community preparation for upcoming data releases, we provide mock catalogs of Gaia exoplanets and planet-impostor binaries. 1 1 https://github.com/CalebLammers/GaiaForecasts
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".