Fast Radio Bursts from White Dwarf Binary Mergers: Isolated and Triple-Induced Channels
Bibliographic record
Abstract
The detection of fast radio bursts (FRBs) in both young and old stellar populations suggests multiple formation pathways, beyond just young magnetars from core-collapse supernovae. A promising delayed channel involves the formation of FRB-emitting neutron stars through merger- or accretion-induced collapse of a massive white dwarf (WD). By simulating a realistic stellar population with both binaries and triples, we identify pathways to WD collapse that could produce FRB candidates. We find that (i) triple dynamics open new merger channels inaccessible to isolated binaries, significantly enhancing the overall merger rate; (ii) triple-induced mergers broaden the delay-time distribution, producing long-delay ($\gtrsim1$-8~Gyr) events largely independent of metallicity, alongside a shorter-delay population ($\lesssim100$~Myr) of rapid mergers; (iii) these long delays naturally yield FRBs in older environments such as quiescent host galaxies and galactic halos; (iv) when convolved with the cosmic star-formation history, binary channels track the star-formation rate ($z_{\rm peak} \sim 2$), while triple channels peak later ($z_{\rm peak} \sim 1$), giving a combined local source rate of $R_0 \approx 2\times10^4~{\rm Gpc^{-3}~yr^{-1}}$, consistent with observations; and (v) applying the same framework to Type~Ia supernovae, we find that triples extend the delay-time tail and roughly double the Ia efficiency relative to binaries, yielding rates and redshift evolution in good agreement with observations. If FRBs originate from the collapse of WDs, our results establish triples, alongside binaries, as a crucial and previously overlooked formation pathway whose predicted rates, host demographics, and redshift evolution offer clear tests for upcoming surveys.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".