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Record W3111340478 · doi:10.3847/1538-3881/abe632

A Distinct Population of Small Planets: Sub-Earths

2021· article· en· W3111340478 on OpenAlexaff
Yansong Qian, Yanqin Wu

Bibliographic record

VenueThe Astronomical Journal · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsPlanetTerrestrial planetEarth radiusAstrobiologyPhysicsPopulationAstronomyAstrophysicsRADIUSPlanetary massExoplanetMagnetosphereMagnetic field

Abstract

fetched live from OpenAlex

Abstract The sizes of small planets are known to be bimodal, with a gap separating planets that have lost their primordial atmospheres (super-Earths) and the ones that retain them (mini-Neptunes). Here, we report evidence for another distinct population at smaller sizes. By focusing on planets orbiting around GK dwarfs inward of 16 days and correcting for observational completeness, we find that the number of super-Earths peaks around 1.4 Earth radii and disappears shortly below this size. Instead, a new population of planets (sub-Earths) appears to dominate at sizes below ∼1 Earth radius, with an occurrence that increases with decreasing size. This pattern is also observed in ultra-short-period planets. The end of super-Earths supports earlier claims that super-Earths and mini-Neptunes, planets that likely form in gaseous protoplanetary disks, have a narrow mass distribution. Sub-Earths, in contrast, can be described by a power-law mass distribution and may be explained by the theory of terrestrial planet formation. We therefore speculate that they are formed well after the gaseous disks have dissipated. The extension of these sub-Earths toward longer orbital periods, currently invisible, may be the true terrestrial analogs. This strongly motivates new searches.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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 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

Citations12
Published2021
Admission routes1
Has abstractyes

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