Fast spinning pulsars as probes of massive black holes’ gravity
Bibliographic record
Abstract
Dwarf galaxies and globular clusters may contain intermediate-mass black holes (103–105 M⊙) in their cores. Estimates of ∼103 neutron stars in the central parsec of the Galaxy and similar numbers in small elliptical galaxies and globular clusters along with an estimated high probability of millisecond (ms)-pulsar formation in those environments have led many workers to propose the use of ms-pulsar timing to measure the mass and spin of intermediate-mass black holes. Models of pulsar motion around a rotating black hole generally assume geodesic motion of a ‘test’ particle in the Kerr metric. These approaches account for well-known effects like de Sitter precession and the Lense–Thirring effect but they do not account for the non-linear effect of the pulsar's stress–energy tensor on the space–time metric. Here we model the motion of a pulsar near a black hole with the Mathisson–Papapetrou–Dixon (MPD) equations. Numerical integration of the MPD equations for black holes of masses 2 × 106, 105 and 103 M⊙ shows that the pulsar will not remain in an orbital plane with motion vertical to the plane being largest relative to the orbit's radial dimensions for the lower mass black holes. The pulsar's out-of-plane motion will lead to timing variations that are up to ∼ 10 μs different from those predicted by planar orbit models. Such variations might be detectable in long-term observations of ms pulsars. If pulsar signals are used to measure the mass and spin of intermediate-mass black holes on the basis of dynamical models of the received pulsar signal, then the out-of-plane motion of the pulsar should be part of that model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".