The Influence of 10 Unique Chemical Elements in Shaping the Distribution of Kepler Planets
Bibliographic record
Abstract
Abstract The chemical abundances of planet-hosting stars offer a glimpse into the composition of planet-forming environments. To further understand this connection, we make the first ever measurement of the correlation between planet occurrence and chemical abundances for ten different elements (C, Mg, Al, Si, S, K, Ca, Mn, Fe, and Ni). Leveraging data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) and Gaia to derive precise stellar parameters ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>σ</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>⋆</mml:mo> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> <mml:mo>≈</mml:mo> <mml:mn>2.3</mml:mn> <mml:mo>%</mml:mo> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>σ</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>⋆</mml:mo> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> <mml:mo>≈</mml:mo> <mml:mn>4.5</mml:mn> <mml:mo>%</mml:mo> </mml:math> ) for a sample of 1018 Kepler Objects of Interest, we construct a sample of well-vetted Kepler planets with precisely measured radii ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>σ</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> <mml:mo>≈</mml:mo> <mml:mn>3.4</mml:mn> <mml:mo>%</mml:mo> </mml:math> ). After controlling for biases in the Kepler detection pipeline and the selection function of the APOGEE survey, we characterize the relationship between planet occurrence and chemical abundance as the number density of nuclei of each element in a star’s photosphere raised to a power, β. varies by planet type, but is consistent within our uncertainties across all ten elements. For hot planets ( P = 1–10 days), an enhancement in any element of 0.1 dex corresponds to an increased occurrence of ≈20% for super-Earths ( R p = 1–1.9 R ⊕ ) and ≈60% for sub-Neptunes ( R p = 1.9–4 R ⊕ ). Trends are weaker for warm ( P = 10–100 days) planets of all sizes and for all elements, with the potential exception of sub-Saturns ( R p = 4–8 R ⊕ ). Finally, we conclude this work with a caution to interpreting trends between planet occurrence and stellar age due to degeneracies caused by Galactic chemical evolution and make predictions for planet occurrence rates in nearby open clusters to facilitate demographics studies of young planetary systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".