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Record W7117256537 · doi:10.62051/azaavz06

Escape Orbits in the Three-Body Problem

2025· article· W7117256537 on OpenAlexaff
Jia Geng

Bibliographic record

VenueTransactions on Computer Science and Intelligent Systems Research · 2025
Typearticle
Language
FieldEngineering
TopicSpacecraft Dynamics and Control
Canadian institutionsSpinal Cord Injury BC
Fundersnot available
KeywordsSpacecraftOrbit (dynamics)Orbital mechanicsCelestial mechanicsField (mathematics)Chaotic

Abstract

fetched live from OpenAlex

The escape orbit in the three-body system is a crucial topic in the field of astrophysics, helping people to research the chaotic system, stimulate the evolution of some complex celestial systems, and design the spacecraft trajectory. Extensive research has been conducted on this topic, but there are still some limitations. This paper focuses on reviewing and analyzing escape orbits in the problem of three bodies. Specifically, this paper explains the basic mechanics conditions and analyzes findings from the general and restricted three-body problems (CRTBP and ERTBP). Key tools such as Poincaré sections, ergodic hypothesis, and fractal basin boundaries are introduced to explain escape mechanisms. The study also highlights recent progress in the escape of a celestial body: spacecraft escape orbit design, relativistic effects on escape, and statistical analysis. The review of gravity-assist and low-energy escape orbit design in this paper provides theoretical support for space missions. Future research should include more comprehensive models, initial conditions, and higher-order physical effects.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.319
Teacher spread0.284 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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