An Excess of Jupiter Analogs in Super-Earth Systems
Bibliographic record
Abstract
Abstract We use radial velocity (RV) observations to search for long-period gas giant companions in systems hosting inner super-Earth (1–4 R ⊕, 1–10 M ⊕) planets to constrain formation and migration scenarios for this population. We consistently refit published RV data sets for 65 stars and find nine systems with statistically significant trends indicating the presence of an outer companion. We combine these RV data with AO images to constrain the masses and semi-major axes of these companions. We quantify our sensitivity to the presence of long-period companions by fitting the sample with a power-law distribution and find an occurrence rate of 39% ± 7% for companions 0.5–20 M Jup and 1–20 au. Half of our systems were discovered by the transit method, and half were discovered by the RV method. While differences in the RV baselines and number of data points between the two samples lead to different sensitivities to distant companions, we find that occurrence rates of gas giant companions in each sample are consistent at the 0.5σ level. We compare the frequency of Jupiter analogs in these systems to the equivalent rate from field star surveys and find that Jupiter analogs are more common around stars hosting super-Earths. We conclude that the presence of outer gas giants does not suppress the formation of inner super-Earths, and that these two populations of planets instead appear to be correlated. We also find that the stellar metallicities of systems with gas giant companions are higher than those without companions, in agreement with the well-established metallicity correlation from RV surveys of field stars.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".