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Record W4400775285 · doi:10.3847/2041-8213/ad5d8e

Probing Dust and Gas Properties Using Ringed Disks

2024· article· en· W4400775285 on OpenAlexaff
Eve J. Lee

Bibliographic record

VenueThe Astrophysical Journal Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsPhysicsPlanetInviscid flowDragAstrophysicsAccretion (finance)TurbulencePopulationMechanics

Abstract

fetched live from OpenAlex

Abstract How rapidly a planet grows in mass and how far it may park from the host star depend sensitively on two nondimensional parameters: Stokes number St and turbulent α. Yet these parameters remain highly uncertain, being difficult or impossible to measure directly. Here, we demonstrate how the ringed disks can be leveraged to obtain St and α separately by constructing a simple toy model that combines the dust radial equation of motion under aerodynamic drag and coupling to gas motion with the measured distribution of dust masses in Class 0/I disks. Focusing on known systems with well-resolved dust rings, we find that the ranges of St and α that are consistent with the measured properties of the rings are small: 10−4 ≲ St ≲ 10−2 and 10−5 ≲ α ≲ 10−3. These low St and α ensure the observed rings are stable against clumping. Even in one marginal case where the formation of bound clumps is possible, further mass growth by pebble accretion is inhibited. Furthermore, the derived low α is consistent with the nearly inviscid regime where type I migration can be prematurely halted. Our analysis predicts a minimal planet population beyond ∼tens of au, where we observe dust rings and significantly more vigorous planet formation inside ∼10 au, consistent with current exo-giant statistics. We close with discussions on the implications of our results on small planet statistics at large orbital distances.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.022
GPT teacher head0.235
Teacher spread0.214 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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