PAIRING OF SUPERMASSIVE BLACK HOLES IN UNEQUAL-MASS GALAXY MERGERS
Bibliographic record
Abstract
We examine the pairing process of supermassive black holes (SMBHs) down to scales of 20–100 pc using a set of N -body/SPH simulations of binary mergers of disk galaxies with mass ratios of 1:4 and 1:10. Our numerical experiments are designed to represent merger events occurring at various cosmic epochs. The initial conditions of the encounters are consistent with the ΛCDM paradigm of structure formation, and the simulations include the effects of radiative cooling, star formation (SF), and supernovae feedback. We find that the pairing of SMBHs depends sensitively on the amount of baryonic mass preserved in the center of the companion galaxies during the last phases of the merger. In particular, due to the combination of gasdynamics and SF, we find that a pair of SMBHs can form efficiently in 1:10 minor mergers, provided that galaxies are relatively gas-rich (gas fractions of 30% of the disk mass) and that the mergers occur at relatively high redshift ( z ∼ 3), when dynamical friction timescales are shorter. Since 1:10 mergers are most common events during the assembly of galaxies, and mergers are more frequent at high redshift when galaxies are also more gas-rich, our results have positive implications for future gravitational wave experiments such as the Laser Interferometer Space Antenna.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".