On the follow-up efforts of long-period transiting planet candidates detected with <i>Gaia</i> astrometry
Bibliographic record
Abstract
ABSTRACT The class of transiting cold Jupiters, orbiting at ≳0.5–1.0 au, is to-date underpopulated. Probing their atmospheric composition and physical characteristics is particularly valuable, as it allows for direct comparisons with the Solar system giant planets. We investigate some aspects of the synergy between Gaia astrometry and other ground-based and space-borne programs for detection and characterization of such companions. We carry out numerical simulations of Gaia observations of systems with one cold transiting gas giant, using Jovian planets around a sample of nearby low-mass stars as proxies. Using state-of-the-art orbit fitting tools, we gauge the potential of Gaia astrometry to predict the time of transit centre Tc for the purpose of follow-up observations to verify that the companions are indeed transiting. Typical uncertainties on Tc will be on the order of a few months, reduced to several weeks for high astrometric signal-to-noise ratios and periods shorter than ∼3 yr. We develop a framework for the combined analysis of Gaia astrometry and radial-velocity data from representative ground-based campaigns and show that combined orbital fits would allow to significantly reduce the transit windows to be searched for, down to about ±2 weeks (2–σ level) in the most favourable cases. These results are achievable with a moderate investment of observing time (∼0.5 nights per candidate, ∼50 nights for the top 100 candidates), reinforcing the notion that Gaia astrometric detections of potentially transiting cold giant planets, starting with Data Release 4, will constitute a valuable sample worthy of synergistic follow-up efforts with a variety of techniques.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".