Bibliographic record
Abstract
The precession of astrophysical jets produced by active-galactic nuclei is likely related to the dynamics of the accretion disks surrounding the central supermassive black holes (BHs) from which jets are launched. Two main mechanisms that can drive jet precession arise from Lense-Thirring precession and tidal torquing. These can explain direct and indirect observations of precessing jets; however, such explanations often utilize crude approximations of the disk evolution and observing jet precession can be challenging with electromagnetic facilities. Simultaneously, the Laser Interferometer Space Antenna (LISA) is expected to measure gravitational waves from the mergers of massive binary BHs with high accuracy and probe their progenitor evolution. In this paper, we connect the LISA detectability of binary BH mergers to the possible jet precession during their progenitor evolution. We make use of a semianalytic model that self-consistently treats disk-driven BH alignment and binary inspiral and includes the possibility of disk breaking. We find that tidal torquing of the accretion disk provides a wide range of jet precession timescales depending on the binary separation and the spin direction of the BH from which the jet is launched. Efficient disk-driven BH alignment results in shorter timescales of ∼ 1 yr which are correlated with higher LISA signal-to-noise ratios. Disk breaking results in the longest possible times of ∼ 10 7 yrs , suggesting a deep interplay between the disk critical obliquity (i.e., where the disk breaks) and jet precession. Studies such as ours will help to reveal the cosmic population of precessing jets that are detectable with gravitational waves. Published by the American Physical Society 2024
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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 | 0.001 |
| 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".