Gravitational Wave Sources in Our Galactic Backyard: Predictions for BHBH, BHNS, and NSNS Binaries Detectable with LISA
Bibliographic record
Abstract
Abstract Future searches for gravitational waves from space will be sensitive to double compact objects in our Milky Way. We present new simulations of the populations of double black holes (BHBHs), BH neutron stars (BHNSs), and double neutron stars (NSNSs) that will be detectable by the planned space-based gravitational-wave detector called Laser Interferometer Space Antenna (LISA). For our estimates, we use an empirically informed model of the metallicity-dependent star formation history of the Milky Way. We populate it using an extensive suite of binary population-synthesis predictions for varying assumptions relating to mass transfer, common-envelope, supernova kicks, remnant masses, and wind mass-loss physics. For a 4(10) yr LISA mission, we predict between 30–370(50–550) detections over these variations, out of which 6–154 (9–238) are BHBHs, 2–198 (3–289) are BHNSs, and 3–35 (4–57) are NSNSs. We expect that about 50% (60%) can be distinguished from double white dwarf sources based on their mass or eccentricity and localization. Specifically, for about 10% (15%), we expect to be able to determine chirp masses better than 10%. For 13% (13%), we expect sky-localizations better than 1°. We discuss how the variations in the physics assumptions alter the distribution of properties of the detectable systems, even when the detection rates are unchanged. We further discuss the possibility of multimessenger observations of pulsar populations with the Square Kilometre Array and assess the benefits of extending the LISA mission.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".