Cold Jupiters and improved masses in 38 Kepler and K2 small planet systems from 3661 HARPS-N radial velocities. No excess of cold Jupiters in small planet systems
Bibliographic record
Abstract
The exoplanet population characterized by relatively short orbital periods ($P<100$ d) around solar-type stars is dominated by super-Earths and sub-Neptunes. However, these planets are missing in our Solar System and the reason behind this absence is still unknown. Two theoretical scenarios invoke the role of Jupiter as the possible culprit: Jupiter may have acted as a dynamical barrier to the inward migration of sub-Neptunes from beyond the water iceline; alternatively, Jupiter may have reduced considerably the inward flux of material (pebbles) required to form super-Earths inside that iceline. Both scenarios predict an anti-correlation between the presence of small planets (SPs) and that of cold Jupiters (CJs) in exoplanetary systems. To test that prediction, we homogeneously analyzed the radial-velocity (RV) measurements of 38 Kepler and K2 transiting SP systems gathered over almost 10 years with the HARPS-N spectrograph, as well as publicly available RVs collected with other facilities. We detected five CJs in three systems, two in Kepler-68, two in Kepler-454, and a very eccentric one in K2-312. We derived an occurrence rate of $9.3^{+7.7}_{-2.9}\%$ for CJs with $0.3-13~M_{Jup}$ and 1-10 AU, which is lower but still compatible at $1.3σ$ with that measured from RV surveys for solar-type stars, regardless of the presence or absence of SPs. The sample is not large enough to draw a firm conclusion about the predicted anti-correlation between SPs and CJs; nevertheless, we found no evidence of previous claims of an excess of CJs in SP systems. As an important by-product of our analyses, we homogeneously determined the masses of 64 Kepler and K2 small planets, reaching a precision better than 5, 7.5 and 10$σ$ for 25, 13 and 8 planets, respectively. Finally, we release the 3661 HARPS-N radial velocities used in this work to the scientific community. [Abridged]
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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 | 0.000 | 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 | 0.001 |
| 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".