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Record W3164715192 · doi:10.3847/1538-4357/ac32c9

Sculpting the Sub-Saturn Occurrence Rate via Atmospheric Mass Loss

2022· article· en· W3164715192 on OpenAlexaff
Tim Hallatt, Eve J. Lee

Bibliographic record

VenueThe Astrophysical Journal · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhysicsSaturnStarsAstrophysicsJovianAccretion (finance)AstronomyPlanet

Abstract

fetched live from OpenAlex

Abstract The sub-Saturn (∼4–8 R ⊕ ) occurrence rate rises with orbital period out to at least ∼300 days. In this work we adopt and test the hypothesis that the decrease in their occurrence toward the star is a result of atmospheric mass loss, which can transform sub-Saturns into sub-Neptunes (≲4 R ⊕ ) more efficiently at shorter periods. We show that under the mass-loss hypothesis, the sub-Saturn occurrence rate can be leveraged to infer their underlying core mass function, and, by extension, that of gas giants. We determine that lognormal core mass functions peaked near ∼10–20 M ⊕ are compatible with the sub-Saturn period distribution, the distribution of observationally inferred sub-Saturn cores, and gas-accretion theories. Our theory predicts that close-in sub-Saturns should be ∼50% less common and ∼30% more massive around rapidly rotating stars; this should be directly testable for stars younger than ≲500 Myr. We also predict that the sub-Jovian desert becomes less pronounced and opens up at smaller orbital periods around M stars compared to solar-type stars (∼0.7 days versus ∼3 days). We demonstrate that exceptionally low-density sub-Saturns, “super-puffs,” can survive intense hydrodynamic escape to the present day if they are born with even larger atmospheres than they currently harbor; in this picture, Kepler 223 d began with an envelope ∼1.5× the mass of its core and is currently losing its envelope at a rate of ∼2 × 10 −3 M ⊕ Myr −1 . If the predictions from our theory are confirmed by observations, the core mass function we predict can also serve to constrain core formation theories of gas-rich 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.200
Teacher spread0.193 · 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.

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

Citations26
Published2022
Admission routes1
Has abstractyes

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