Measuring the Recoverability of Close Binaries in Gaia DR2 with the Robo-AO Kepler Survey
Bibliographic record
Abstract
Abstract We use the Robo-AO survey of Kepler planetary candidate host stars, the largest adaptive optics survey yet performed, to measure the recovery rate of close stellar binaries in Gaia DR2. We find that Gaia recovers binaries down to 1″ at magnitude contrasts as large as six; closer systems are not resolved, regardless of secondary brightness. Gaia DR2 binary detection does not have a strong dependence on the orientation of the stellar pairs. We find 177 nearby stars to Kepler planetary candidate host stars in Gaia DR2 that were not detected in the Robo-AO survey, almost all of which are faint ( G > 20); the remainder were largely targets observed by Robo-AO in poor conditions. If the primary star is the host, the impact on the radii estimates of planet candidates in these systems is likely minimal; many of these faint stars, however, could be faint eclipsing binaries that are the source of a false positive planetary transit signal. With Robo-AO and Gaia combined, we find that 18.7 ± 0.7% of Kepler planet candidate hosts have nearby stars within 4″. We also find 36 nearby stars in Gaia DR2 around 35 planetary candidate host stars detected with K2 . The nearby star fraction rate for K2 planetary candidates is significantly lower than that for the primary Kepler mission. The binary recovery rate of Gaia will improve initial radius estimates of future Transiting Exoplanet Survey Satellite planet candidates significantly; however, ground-based high-resolution follow-up observations are still needed for precise characterization and confirmation. The sensitivity of Gaia to closely separated binaries is expected to improve in later data releases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".