Improved early warning of compact binary mergers using higher modes of gravitational radiation: a population study
Bibliographic record
Abstract
ABSTRACT A gravitational wave early warning of a compact binary coalescence event, with a sufficiently tight localization skymap, would allow telescopes to point in the direction of the potential electromagnetic counterpart before its onset. Use of higher modes of gravitational radiation, in addition to the dominant mode typically used in templated real-time searches, was recently shown to produce significant improvements in early-warning times and skyarea localizations for a range of asymmetric mass binaries. We perform a large-scale study to assess the benefits of this method for a population of compact binary merger observations. In particular, we inject 100 000 such signals in Gaussian noise, with component masses $m_1 \in \left[1, 60 \right] \, \mathrm{M}_{\odot }$ and $m_2 \in \left[1, 3 \right] \, \mathrm{M}_{\odot }$. We consider three scenarios involving ground-based detectors: the fifth (O5) observing run of the Advanced LIGO-Virgo-KAGRA network, its projected Voyager upgrade, as well as a proposed third-generation (3G) network. We find that for fixed early-warning times of 20–60 s, the inclusion of the higher modes can provide localization improvements of a factor of ≳2 for up to ${\sim}60{{\ \rm per\ cent}}$ ($70 {{\ \rm per\ cent}}$) of the neutron star–black hole (NSBH) systems in the O5 (Voyager) scenario. Considering only those NSBH systems that can produce potential electromagnetic counterparts, such improvements in the localization can be expected for ${\sim}5\!-\!35{{\ \rm per\ cent}}$ $(20\!-\!50{{\ \rm per\ cent}})$ binaries in O5 (Voyager). For the 3G scenario, a significant fraction of the events have time gains of a minute to several minutes, assuming fiducial target localization areas of 100–1000 deg2.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".