Self-Interacting Dark Matter Solves the Final Parsec Problem of Supermassive Black Hole Mergers
Bibliographic record
Abstract
Evidence for a stochastic gravitational wave (GW) background, plausibly originating from the merger of supermassive black holes (SMBHs), is accumulating with observations from pulsar timing arrays. An outstanding question is how inspiraling SMBHs get past the "final parsec" of separation, where they have a tendency to stall before GW emission alone can make the binary coalesce. We argue that dynamical friction from the dark matter (DM) spike surrounding the black holes is sufficient to resolve this puzzle, if the DM has a self-interaction cross section of order cm^{2}/g. The same effect leads to a softening of the GW spectrum at low frequencies as suggested by the current data. For collisionless cold DM, the friction deposits so much energy that the spike is disrupted and cannot bridge the final parsec, while for self-interacting DM, the isothermal core of the halo can act as a reservoir for the energy liberated from the SMBH orbits. A realistic velocity dependence, such as generated by the exchange of a massive mediator like a dark photon, is favored to give a good fit to the GW spectrum while providing a large enough core. A similar velocity dependence has been advocated for solving the small-scale structure problems of cold DM.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".