Improved early-warning estimates of luminosity distance and orbital inclination of compact binary mergers using higher modes of gravitational radiation
Bibliographic record
Abstract
ABSTRACT The pre-merger (early-warning) gravitational-wave (GW) detection and localization of a compact binary merger would enable astronomers to capture potential electromagnetic (EM) emissions around the time of the merger, thus shedding light on the complex physics of the merger. While early detection and sky localization are of primary importance to the multimessenger follow-up, improved estimates of luminosity distance and orbital inclination could also provide insights on the observability of EM emission. In this work, we demonstrate that the inclusion of higher modes of gravitational radiation, which vibrate at higher multiples of the orbital frequency than the dominant mode, would significantly improve the early-warning estimates of the luminosity distance and orbital inclination of asymmetric compact binaries, in particular, neutron-star black hole mergers. This will help astronomers to better determine their follow-up strategy of such systems. Focusing on future observing runs of the ground-based GW detectors, we show that for neutron-star black hole binaries that are potentially EM-bright, the inclusion of higher modes improves the luminosity distance estimates by a factor of ∼1 − 1.5 (1.1 − 2) [1.1 − 5] for the O5 (Voyager) [3G] observing scenario, 45 (45) [300] s before the merger. There are significant improvements in orbital inclination estimates as well. We also investigate these improvements with varying sky-location and polarization angle. Combining the luminosity distance uncertainties with localization skyarea estimates, we find that the number of galaxies within localization volume is reduced by a factor of ∼1 − 2.5 (1.2 − 4) [1.2 − 10] with the inclusion of higher modes in O5 (Voyager) [3G].
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".