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Record W4410967653 · doi:10.3847/1538-4357/adc1c4

A Unified, Physical Framework for Mean Motion Resonances

2025· article· en· W4410967653 on OpenAlexaff
Daniel Tamayo, Sam Hadden

Bibliographic record

VenueThe Astrophysical Journal · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum chaos and dynamical systems
Canadian institutionsCanadian Institute for Theoretical Astrophysics
Fundersnot available
KeywordsPhysicsMotion (physics)Mean motionClassical mechanicsAstrophysicsPlanet

Abstract

fetched live from OpenAlex

Abstract The traditional approach to analyzing mean motion resonances (MMRs) is through the canonical perturbation theory. While this is a powerful method, its generality leads to complicated combinations of variables that are challenging to interpret and require looking up numerical coefficients particular to every different resonance. In this paper, we develop simpler scaling relations in the limit where orbits are closely spaced (period ratios ≲2), and interplanetary interactions can be approximated by only considering the close approaches each time the inner planet overtakes the outer at the conjunction. We develop geometric arguments for several powerful results: (i) that p : p − q MMRs of the same order q are all rescaled versions of one another, (ii) that the general case of two massive planets on closely spaced, eccentric, coplanar orbits can be approximately mapped onto the much simpler case of an eccentric test particle perturbed by a massive planet on a coplanar circular orbit, and (iii) that, while the effects of consecutive conjunctions add up coherently for first-order ( p : p − 1) MMRs, they partially cancel for p : p − q MMRs with order q > 1, providing a physical explanation for why these higher-order MMRs are weaker and can often be ignored. Finally, we provide simple expressions for the widths of MMRs and their associated oscillation frequencies that are universal to all closely spaced MMRs of a given order q , in the pendulum approximation.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.639
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.010
GPT teacher head0.274
Teacher spread0.263 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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