Prospects for Measuring Off-axis Spins of Binary Black Holes with Plus-era Gravitational-wave Detectors
Bibliographic record
Abstract
Abstract The mass and spin properties of binary black holes (BBHs) inferred from their gravitational-wave signatures reveal important clues about how these systems form. BBHs originating from isolated binary evolution are expected to have spins preferentially aligned with their orbital angular momentum, whereas there is no such preference in binaries formed via dynamical assembly. The fidelity with which near-future gravitational-wave detectors can measure off-axis spins will have implications for the study of BBH formation channels. In this work, we examine the degree to which the Advanced LIGO Plus (A+) and Advanced Virgo Plus (AdV+) interferometric detectors can measure both aligned and misaligned spins. We compare spin resolution between the LIGO-Virgo network operating at either A+/AdV+ (“Plus”) sensitivity or Advanced-era design (“Design”) sensitivity using simulated BBH gravitational-wave signals injected into synthetic detector noise. The signals are distributed over the mass-spin parameter space of likely BBH systems, accounting for the effects of precession and higher-order modes. We find that the Plus upgrades yield significant improvements in spin estimation for systems with unequal masses and moderate or large spins. Using simulated signals modeled after different types of hierarchical BBH mergers, we also conclude that the Plus detector network will yield substantially improved spin estimates for 1G+2G binaries compared to the Design network.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".