Associating fast radio bursts with compact binary mergers via gravitational lensing
Bibliographic record
Abstract
ABSTRACT The origin of fast radio bursts (FRBs) is currently an open question with several proposed sources and corresponding mechanisms for their production. Among them are compact binary coalescences (CBCs) that also generate gravitational waves (GWs). Spatial and temporal coincidences between GWs and FRBs have so far been used to search for potential FRB counterparts to GWs from CBCs. However, such methods suffer from relatively poor sky-localization of the GW sources, and similarly poor luminosity distance estimates of both GW and FRB sources. The expected time-delay between the GW and radio emission is also poorly understood. In this work, we propose an astrophysical scenario that could potentially provide an unambiguous association between CBCs and FRBs, if one exists, or unambiguously rule out FRB counterparts to a given CBC GW event. We demonstrate that if a CBC that emitted both GWs and FRBs is gravitationally lensed, we can make a >5σ association using time-delay estimates of the lensed GW and FRB images (in strong lensing), which are expected to be measured with millisecond (for GW) and nano-second (FRB) precisions. We also demonstrate that the CBC–FRB association can be made in the microlensing regime as well where wave-optics effects modulate the GW waveform. We further investigate the rate of such detected associations in future observing scenarios of both GW and radio detectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".