<scp>trace</scp>: a code for time-reversible astrophysical close encounters
Bibliographic record
Abstract
ABSTRACT We present trace, an almost time-reversible hybrid integrator for the planetary N-body problem. Like hybrid symplectic integrators, trace can resolve close encounters between particles while retaining many of the accuracy and speed advantages of a fixed time-step symplectic method such the Wisdom–Holman map. trace switches methods time-reversibly during close encounters following the prescription of Hernandez & Dehnen. In this paper we describe the derivation and implementation of trace and study its performance for a variety of astrophysical systems. In all our test cases, trace is at least as accurate and fast as the hybrid symplectic integrator mercurius. In many cases, trace’s performance is vastly superior to that of mercurius. In test cases with planet–planet close encounters, trace is as accurate as mecurius with a 12× speed-up. If close encounters with the central star are considered, trace achieves good error performance while mercurius fails to give qualitatively correct results. In ensemble tests of violent scattering systems, trace matches the high-accuracy IAS15 while providing a 15× speed-up. In large N systems simulating lunar accretion, trace qualitatively gives the same results as ias15 but at a 41× speed-up. We also discuss some cases such as von Zeipel–Lidov–Kozai cycles where hybrid integrators perform poorly and provide some guidance on which integrator to use for which system. trace is freely available within the rebound package.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".