The influence of spin in black hole triplets
Bibliographic record
Abstract
Spin can influence the dynamics of the already chaotic black hole triplet system. We follow this problem in two sets of simulations: first, the Agekian–Anosova region (or region D), and second, using Pythagorean triangles. We use ARCcode, an N-body code that performs numerical integration of orbits. This code includes post-Newtonian corrections, which we include up to the 2.5th order. In set one of our simulations, we fix the masses of the black holes at 10 6 M ⊙ . Then we run the simulations first without any spin added and after by initialising spin on one of the black holes. We find that after including spin into the system, 12.9% of the simulations changed outcomes. Either the systems went from having all black holes merging to having a black hole escaping the system, or vice versa. In the second set of simulations, we expanded into Pythagorean triangles as initial positions of black holes, stemming from Burrau’s three-body problem. We varied the masses of the black holes from 10 0 M ⊙ –10 12 M ⊙ . Black holes in these systems were given spin in normalised units ranging from 0 to ∼ 0.95. We find that intermediate mass black holes in the range of 10 4 M ⊙ –10 5 M ⊙ , were influenced the most by spin, particularly in their lifetimes. We also find that simulations, initialised as 2-dimensional, become 3-dimensional.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".