Predicting the Rate of Fast Radio Bursts in Globular Clusters from Binary Black Hole Observations
Bibliographic record
Abstract
Abstract The repeating fast radio burst (FRB) source in an old globular cluster (GC) in M81 proves that FRBs, which are typically associated with young magnetars, can also occur in old stellar populations. A potential explanation is super-Chandrasekhar binary white dwarf (BWD) coalescences, which may produce FRB-emitting neutron stars. GCs can also give rise to binary black hole (BBH) mergers detectable with gravitational waves, and the BWD coalescence rate from GCs is correlated with their BBH merger rate. For the first time, we combine independent observations of gravitational waves and FRBs to infer the origins of FRB sources. We use GC formation histories inferred from BBH observations to predict the rate of super-Chandrasekhar BWD coalescences originating from GCs as a function of redshift. We explore mass-loss and mass-conserved scenarios for BWD coalescences and find that the coalescence rates evolve differently across redshift in these two cases. In the mass-loss scenario, the BWD coalescence rates decrease with increasing redshift, similar to some recent measurements of the FRB rate as a function of redshift. We show that GCs could contribute ≲1% to the total FRB source formation rates in the local Universe. Our multimessenger approach also offers a novel method to better constrain the GC population using both FRB and gravitational-wave observations.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".