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Record W3204360321 · doi:10.3847/2041-8213/ac2947

Why Do M Dwarfs Have More Transiting Planets?

2021· article· en· W3204360321 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal Letters · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversity of Victoria
FundersAgencia Nacional de Investigación y Desarrollo
KeywordsPlanetExoplanetStarsAccretion (finance)Giant planetBrown dwarfPlanetary systemSnow line

Abstract

fetched live from OpenAlex

Abstract We propose a planet formation scenario to explain the elevated occurrence rates of transiting planets around M dwarfs compared to Sun-like stars discovered by Kepler. We use a pebble drift and accretion model to simulate the growth of planet cores inside and outside of the snow line. A smaller pebble size interior to the snow line delays the growth of super-Earths, allowing giant planet cores in the outer disk to form first. When those giant planets reach pebble isolation mass they cut off the flow of pebbles to the inner disk and prevent the formation of close-in super-Earths. We apply this model to stars with masses between 0.1 and 2 M ⊙ and for a range of initial disk masses. We find that the masses of hot super-Earths and of cold giant planets are anticorrelated. The fraction of our simulations that form hot super-Earths is higher around lower-mass stars and matches the exoplanet occurrence rates from Kepler. The fraction of simulations forming cold giant planets is consistent with the stellar mass dependence from radial-velocity surveys. A key testable prediction of the pebble accretion hypothesis is that the occurrence rates of super-Earths should decrease again for M dwarfs near the substellar boundary like Trappist-1.

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 score0.298
Threshold uncertainty score0.617

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)0.0000.000

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 head0.009
GPT teacher head0.216
Teacher spread0.206 · 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

Citations68
Published2021
Admission routes1
Has abstractyes

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