Modeling Current and Future High-cadence Surveys of Repeating Fast Radio Burst Populations
Bibliographic record
Abstract
Abstract In recent years, the Canadian Hydrogen Intensity Mapping Experiment (CHIME) interferometer has revealed a large number of fast radio bursts (FRBs), including a sizable population that demonstrates repeating behavior. This transit facility, employing a real-time FRB search pipeline, continually scans the sky with declinations between −10° and 90° for events with fluences ⪆0.4 Jy ms. We simulate a population of repeating FRBs by performing Monte Carlo simulations of underlying source populations processed through a mock CHIME/FRB observing pipeline. Assuming intrinsic repeater rates follow a Poisson distribution, we test assumptions about the burst populations of the repeater sample, and construct models of the FRB sample assuming various cosmological distributions. We infer the completeness of CHIME/FRB observations as a function of observing cadence and redshifts out to 0.5. We find that, if all simulated bursts have a fixed Poisson probability of repetition over their integrated time of observation, repeating burst detections across comoving volume should continue to grow near linearly on the order of decades. We predict that around 170 of the current CHIME/FRB one-off sources will ultimately repeat. We also make projections for FRB repeaters by future facilities and demonstrate that the number of repeaters they find could saturate on a ∼3 yr timescale.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".