Connections between Planetary Populations and Chemical Characteristics of Their Host Stars
Bibliographic record
Abstract
Abstract Chemical anomalies in planet-hosting stars (PHSs) are studied in order to assess how the planetary nature and multiplicity affect the atmospheric chemical abundances of their host stars. We employ APOGEE DR17 to select thin-disk stars of the Milky Way, and crossmatch them with the Kepler Input Catalog to identify confirmed PHSs, which results in 227 PHSs with available chemical abundance ratios for six refractory elements. We also examine an ensemble of stars without planet signals, which are equivalent to the selected PHSs in terms of evolutionary stage and stellar parameters, to correct for Galactic chemical evolution effects, and derive the abundance gradient of refractory elements over the condensation temperature for the PHSs. Using the Galactic chemical evolution corrected abundances, we find that our PHSs do not show a significant difference in abundance slope from the stars without planets. However, when we examine the trends of the refractory elements of PHSs, based on the total number of their planets and their planet types, we find that the PHSs with giant planets are more depleted in refractory elements than those with rocky planets. Among the PHSs with rocky planets, the refractory depletion trends are potentially correlated with the terrestrial planets’ radii and multiplicity. In the cases of PHSs with giant planets, sub-Jovian PHSs demonstrate more depleted refractory trends than stars hosting Jovian-mass planets, raising questions on different planetary formation processes for Neptune-like and Jupiter-like planets.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".