Constraints on planets in nearby young moving groups detectable by high-contrast imaging and <i>Gaia</i> astrometry
Bibliographic record
Abstract
ABSTRACT The formation of giant planets can be studied through direct imaging by observing planets both during and after formation. Giant planets are expected to form either by core accretion, which is typically associated with low initial entropy (cold-start models) or by gravitational instability, associated with high initial entropy of the gas (hot-start models). Thus, constraining the initial entropy can provide insight into a planet’s formation process and determines the resultant brightness evolution. In this study, we find that, by observing planets in nearby moving groups of known age both through direct imaging and astrometry with Gaia, it will be possible to constrain the initial entropy of giant planets. We simulate a set of planetary systems in stars in nearby moving groups identified by BANYAN Σ and assume a model for planet distribution consistent with radial-velocity detections. We find that Gaia should be able to detect approximately 25 per cent of planets in nearby moving groups greater than $\sim 0.3\, M_\text{J}$. Using 5σ contrast limits of current and future instruments, we calculate the flux uncertainty, and using models for the evolution of the planet brightness, we convert this to an initial entropy uncertainty. We find that future instruments such as METIS on E-ELT as well as GRAVITY and VIKiNG with VLTI should be able to constrain the entropy to within 0.5 kB/baryon, which implies that these instruments should be able to distinguish between hot- and cold-start models.
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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".