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Record W4327725299 · doi:10.3847/2041-8213/acbd35

Removal of Hot Saturns in Mass–Radius Plane by Runaway Mass Loss

2023· article· en· W4327725299 on OpenAlexaff
Daniel Thorngren, Eve J. Lee, Eric Lopez

Bibliographic record

VenueThe Astrophysical Journal Letters · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsMcGill UniversityUniversité du QuébecUniversité de MontréalMontreal Clinical Research Institute
Fundersnot available
KeywordsRADIUSPlanetPopulationPhysicsAstrophysicsInflation (cosmology)Astronomy

Abstract

fetched live from OpenAlex

Abstract The hot Saturn population exhibits a boundary in mass–radius space, such that no planets are observed at a density less than ∼0.1 g cm −3 . Yet, planet interior structure models can readily construct such objects as the natural result of radius inflation. Here, we investigate the role X-ray and extreme UV irradiation (XUV)-driven mass loss plays in sculpting the density boundary by constructing interior structure models that include radius inflation, photoevaporative mass loss, and a simple prescription of Roche lobe overflow. We demonstrate that planets puffier than ∼0.1 g cm −3 experience a runaway mass loss caused by adiabatic radius expansion as the gas layer is stripped away, providing a good explanation of the observed edge in mass–radius space. The process is also visible in the radius–period and mass–period spaces, though smaller, high-bulk-metallicity planets can still survive at short periods, preserving a partial record of the population distribution at formation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.000
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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations32
Published2023
Admission routes1
Has abstractyes

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