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Record W2954751010 · doi:10.3847/1538-3881/ab7814

On the Patterns Observed in Kepler Multi-planet Systems

2020· article· en· W2954751010 on OpenAlexaff

Bibliographic record

VenueThe Astronomical Journal · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsCanadian Institute for Theoretical Astrophysics
Fundersnot available
KeywordsPlanetPlanetary systemOrbital periodTransit (satellite)KeplerLimit (mathematics)Stability (learning theory)Star (game theory)

Abstract

fetched live from OpenAlex

Abstract Recent studies claimed that planets around the same star have similar sizes and masses and regular spacings, and that planet pairs usually show ordered sizes such that the outer planet is usually the larger one. Here I show that these patterns can be largely explained by detection biases. The Kepler planet detections are set by the transit signal-to-noise ratio (S/N). For different stellar properties and orbital period values, the same S/N corresponds to different planetary sizes. This variation in the detection threshold naturally leads to apparent correlations in planet sizes and the observed size ordering. The apparently correlated spacings, measured in period ratios, between adjacent planet pairs in systems with at least three detected planets are partially due to the arbitrary upper limit that the earlier study imposed on the period ratio, and partially due to the varying stability threshold for different planets. After these detection biases are taken into account, we do not find strong evidence for the so-called intra-system uniformity or the size ordering effect. Instead, the physical properties of Kepler planets are largely independent of the properties of their siblings and the parent star. It is likely that the dynamical evolution has erased the memory of Kepler planets about their initial formation conditions. In other words, it will be difficult to infer the initial conditions from the observed properties and the architecture of Kepler planets.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.049
GPT teacher head0.228
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2020
Admission routes1
Has abstractyes

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