Synthesising the repeating FRB population using frbpoppy
Bibliographic record
Abstract
The observed fast radio burst (FRB) population can be divided into one-off and repeating FRB sources. Either this division is a true dichotomy of the underlying sources, or selection effects and low activity prohibit us from observing repeat pulses from all constituents making up the FRB source population. We attempted to break this degeneracy through FRB population synthesis. With that aim in mind, we extended frbpoppy (which previously only handled one-off FRBs) to also simulate repeaters. We next modelled the Canadian Hydrogen Intensity Mapping Experiment FRB survey (CHIME/FRB). Using this implementation, we investigated the impact of luminosity functions on the observed dispersion measure (DM) and distance distributions of both repeating and one-off FRBs. We show that for a single, intrinsically repeating source population with a steep luminosity function, selection effects should shape the DM distributions of one-off and repeating FRB sources differently. This difference is not yet observed. We next show how the repeater fraction over time can help in determining the repetition rate of an intrinsic source population. We simulated this fraction for CHIME/FRB, and we show that a source population comprised solely of repeating FRBs can describe CHIME/FRB observations with the use of a flat luminosity function. From the outcome of these two methods, we thus conclude that all FRBs originate from a single and mostly uniform population of varying repeaters. Within this population, the luminosity function cannot be steep, and there must be minor differences in physical or behaviour parameters that correlate with the repetition rate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".