Statistical properties of fast radio bursts elucidate their origins: magnetars are favored over gamma-ray bursts
Bibliographic record
Abstract
Abstract Fast radio bursts (FRBs) are extremely strong radio flares lasting several milliseconds, most of which come from unidentified objects at a cosmological distance. They can be apparently repeating or not. In this paper, we analyzed 18 repeaters and 12 non-repeating FRBs observed in the frequency bands of 400–800 MHz from Canadian Hydrogen Intensity Mapping Experiment (CHIME). We investigated the distributions of FRB isotropic-equivalent radio luminosity, considering the K correction. Statistically, the luminosity distribution can be better fitted by Gaussian form than by power-law. Based on the above results, together with the observed FRB event rate, pulse duration, and radio luminosity, FRB origin models are evaluated and constrained such that the gamma-ray bursts (GRBs) may be excluded for the non-repeaters while magnetars or neutron stars (NSs) emitting the supergiant pulses are preferred for the repeaters. We also found the necessity of a small FRB emission beaming solid angle (about 0.1 sr) from magnetars that should be considered, and/or the FRB association with soft gamma-ray repeaters (SGRs) may lie at a low probability of about 10%. Finally, we discussed the uncertainty of FRB luminosity caused by the estimation of the distance that is inferred by the simple relation between the redshift and dispersion measure (DM).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".