Gear Up for the Action Replay: Leveraging Lensing for Enhanced Gravitational-wave Early Warning
Bibliographic record
Abstract
Abstract Premerger gravitational-wave (GW) sky localization of binary neutron star (BNS) and neutron star–black hole (NSBH) coalescence events would enable telescopes to capture precursors and electromagnetic (EM) emissions around the time of the merger. We propose an astrophysical scenario that could provide early-warning times of hours to days before coalescence with subarcsecond localization, provided that these events are gravitationally lensed. The key idea is that if the BNS/NSBH is lensed, then so is the host galaxy identified via the EM counterpart. From the angular separation of the lensed host galaxy images, as well as its redshift and the (foreground) lens redshift, we demonstrate that, for galaxy-scale lenses, we can predict the time delays/arrival time differences assuming a standard lens model. We further assess the feasibility and benefits of lensing as a tool for early warning in various GW observing runs of the LIGO–Virgo–Kagra network, including Voyager and the third-generation network. To that end, we study the effect of the limited angular resolution of the telescopes on our ability to predict the time delays. We find that with an angular resolution of 0.05″ we can predict time delays of >1 day with 1σ error bar of (hours) at best. We also construct realistic time delay distributions of detectable lensed BNSs/NSBHs to forecast the early-warning times we might expect in the observing scenarios we consider.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".