Formation of LISA Black Hole Binaries in Merging Dwarf Galaxies: The Imprint of Dark Matter
Bibliographic record
Abstract
Abstract Theoretical models for the expected merger rates of intermediate-mass black holes (IMBHs) are vital for planned gravitational-wave detection experiments such as the Laser Interferometer Space Antenna (LISA). Using collisionless N -body simulations of dwarf galaxy (DG) mergers, we examine how the orbital decay of IMBHs and the efficiency of IMBH binary formation depend on the central dark matter (DM) density profile of the merging DGs. Specifically, we explore various asymptotic inner slopes γ of the DG’s DM density distribution, ranging from steep cusps ( γ = 1) to shallower density profiles ( γ < 1), motivated by well-known baryonic-feedback effects as well as by DM models that differ from cold DM at the scales of DGs. We find that the inner DM slope is crucial for the formation (or lack thereof) of an IMBH binary; only mergers between DGs with cuspy DM profiles ( γ = 1) are favorable to forming a hard IMBH binary, whereas when γ < 1 the IMBHs stall at a separation of 50–100 pc. Consequently, the rate of LISA signals from IMBH coalescence will be determined by the fraction of DGs with a cuspy DM profile. Conversely, the LISA event rates at IMBH mass scales offer in principle a novel way to place constraints on the inner structure of DM halos in DGs and address the core–cusp controversy. We also show that, with spatial resolutions of ∼0.1 kpc, as often adopted in cosmological simulations, all IMBHs stall, independent of γ . This suggests caution should be taken when employing cosmological simulations of galaxy formation to study BH dynamics in DGs.
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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) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".