Tidal disruption events by compact supermassive black hole binaries
Bibliographic record
Abstract
ABSTRACT Stars can be tidally destroyed or swallowed by supermassive black hole binaries (SMBHBs). Using a large number of few-body simulations, we investigate the enhancement and suppression of full and partial disruption and direct capture events by hard SMBHBs with wide ranges of key parameters, i.e. the primary BH mass ($M_{\rm BH, 1}= 10^{5}{-}10^{8}\, {\rm M}_{\odot }$), the binary mass ratio (10−3–1), the ratio of the binary semimajor axis to the hardening radius (10−4–1), the binary eccentricity (0.0–0.9) and the stellar mass $(0.3{-}3\, {\rm M}_{\odot})$. This is a significant extension of the parameter space compared to previous work. We show that the encounter probabilities of all three events are well-described by the encounter cross-section. The probability of full tidal disruption events (FTDEs) by SMBHBs can be enhanced by up to a factor of 40–50 or suppressed by up to a factor of 10, relative to that by single BHs, depending on the binary parameters. Relativistic effects can provide an additional enhancement of the FTDE probability by less than a factor of 2–3 for $M_{\rm BH, 1}> 10^{7}\, {\rm M}_{\odot }$. We provide a fitting formula for the FTDE probability by SMBHBs that works for a wide range of parameters. Partial disruption events can occur multiple times before FTDEs or direct captures, and their probabilities can be greater than that of FTDEs by a factor of three. Because partial disruption events can induce stellar spins and mass loss, and change the orbits, it can significantly affect the overall FTDE rate and the shape of the light curves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".