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Record W4380766679 · doi:10.3847/psj/acd771

OSSOS. XXVII. Population Estimates for Theoretically Stable Centaurs between Uranus and Neptune

2023· article· en· W4380766679 on OpenAlexaff
Rosemary C. Dorsey, Michele T. Bannister, Samantha Lawler, A. H. Parker

Bibliographic record

VenueThe Planetary Science Journal · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsCampion CollegeUniversity of Regina
FundersMarsden Fund
KeywordsCentaurUranusEclipticNeptuneSolar SystemTrans-Neptunian objectPhysicsPopulationLibration (molecule)AstrophysicsAstronomyNice modelOrbiterAstrometryPlanetPlanetary systemGeometryPlanetary migrationSolar windMathematicsStars

Abstract

fetched live from OpenAlex

Abstract We calculate the upper bounds of the population of theoretically stable Centaur orbits between Uranus and Neptune. These small bodies are on low-eccentricity, low-inclination orbits in two specific bands of semimajor axis, centered at ∼24.6 and ∼25.6 au. They exhibit unusually long Gyr-stable lifetimes in previously published numerical integrations, orders of magnitude longer than that of a typical Centaur. Despite the increased breadth and depth of recent Solar System surveys, no such objects have been found. Using the Outer Solar System Origins Survey simulator to calculate the detection efficiency for these objects in an ensemble of fully characterized surveys, we determine that a population of 72 stable Centaurs with absolute magnitude H r ≤ 10 (95% confidence upper limit) could remain undetected. The upcoming Legacy Survey of Space and Time will be able to detect this entire intrinsic population, due to its complete coverage of the ecliptic plane. If detected, these objects will be interesting dynamically accessible mission targets—especially as comparison of the stable Centaur orbital phase space to the outcomes of several modern planetary migration simulations suggests that these objects could be close to primordial in nature.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.904

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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