On Detecting Repetition from Fast Radio Bursts
Bibliographic record
Abstract
Abstract Fast radio bursts (FRBs) are bright, millisecond-duration radio pulses of unknown origin. To date, only one (FRB 121102) out of several dozen has been seen to repeat, though the extent to which it is exceptional remains unclear. We discuss detecting repetition from FRBs, which will be very important for understanding their physical origin, and which also allows for host galaxy localization. We show how the combination of instrument sensitivity, beam shapes, and individual FRB luminosity functions affect the detection of sources with repetition that is not necessarily described by a homogeneous Poisson process. We demonstrate that the Canadian Hydrogen Intensity Mapping Experiment (CHIME) could detect many new repeating FRBs for which host galaxies could be subsequently localized using other interferometers, but it will not be an ideal instrument for monitoring FRB 121102. If the luminosity distributions of repeating FRBs are given by power laws with significantly more dim than bright bursts, CHIME’s repetition discoveries could preferentially come not from its own discoveries, but from sources first detected with lower-sensitivity instruments like the Australian Square Kilometer Array Pathfinder in fly’s eye mode. We then discuss observing strategies for upcoming surveys, and advocate following up sources at approximately regular intervals and with telescopes of higher sensitivity when possible. Finally, we discuss doing pulsar-like periodicity searching on FRB follow-up data, based on the idea that while most pulses are undetectable, folding on an underlying rotation period could reveal the hidden signal.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".