LISA and the Existence of a Fast-merging Double Neutron Star Formation Channel
Bibliographic record
Abstract
Abstract Using a Milky Way (MW) double neutron star (DNS) merger rate of 210 Myr−1, as derived by the Laser Interferometer Gravitational-Wave Observatory (LIGO), we demonstrate that the Laser Interferometer Space Antenna (LISA) will detect on average 240 (330) DNSs within the MW for a 4 yr (8 yr) mission with a signal-to-noise ratio greater than 7. Even adopting a more pessimistic rate of 42 Myr−1, as derived by the population of Galactic DNSs, we find a significant detection of 46 (65) MW DNSs. These DNSs can be leveraged to constrain formation scenarios. In particular, without prior information on a particular system’s position and orbital period, traditional NS-discovery methods using radio telescopes alone are insensitive to DNSs with P orb ≲ 1 hr (merger times ≲10 Myr). If a fast-merging channel exists that forms DNSs at these short orbital periods, LISA affords, perhaps, the best opportunity to observationally identify and characterize these systems; we show that toy models for possible formation scenarios leave imprints on DNS orbital eccentricities, which may be measured by LISA for values as small as ∼10−2.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".