Which bright fast radio bursts repeat?
Bibliographic record
Abstract
ABSTRACT A handful of fast radio bursts (FRBs) are now known to repeat. However, the question remains – do they all? We report on an extensive observational campaign with the Australian Square Kilometre Array Pathfinder (ASKAP), Parkes, and Robert C. Byrd Green Bank Telescope, searching for repeat bursts from FRBs detected by the Commensal Real-time ASKAP Fast Transients survey. In 383.2 h of follow-up observations covering 27 FRBs initially detected as single bursts, only two repeat bursts from a single FRB, FRB 171019, were detected, which have been previously reported by Kumar et al. We use simulations of repeating FRBs that allow for clustering in burst arrival times to calculate new estimates for the repetition rate of FRB 171019, finding only slight evidence for incompatibility with the properties of FRB 121102. Our lack of repeat bursts from the remaining FRBs set limits on the model of all bursts being attributable to repeating FRBs. Assuming a reasonable range of repetition behaviour, at most 60 per cent (90 per cent confidence limit) of these FRBs have an intrinsic burst distribution similar to FRB 121102. This result is shown to be robust against different assumptions on the nature of repeating FRB behaviour, and indicates that if indeed all FRBs repeat, the majority must do so very rarely.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".