A statistical analysis of fluence and energy distributions of non-repeating fast radio bursts detected by CHIME
Bibliographic record
Abstract
ABSTRACT Fast radio bursts (FRBs) are energetic radio bursts that typically last for milliseconds. They are mostly of extragalactic origin, but the progenitors, trigger mechanisms, and radiation processes are still largely unknown. Here, we present a comprehensive analysis on 415 non-repeating FRBs detected by CHIME (Canadian Hydrogen Intensity Mapping Experiment), applying manual filtering to ensure sample completeness. It is found that the distribution of fluence can be approximated by a three-segment power-law function, with the power-law indices being $-3.76 \pm 1.61$, $0.20 \pm 0.68$, and $2.06 \pm 0.90$ in the low, middle, and high-fluence segments, respectively. Both the total dispersion measure (DM) and the extragalactic DM follow a smoothly broken power-law distribution, with characteristic break DM values of $\sim 703$ and $\sim 639$ pc $\mathrm {cm}^{-3}$, respectively. The redshifts are estimated from the extragalactic DM by using the Macquart relation, which are found to peak at $z \sim 0.6$. The isotropic energy release ($E_{\text{iso}}$) is also derived for each burst. Two-Gaussian components are revealed in the distribution of $E_{\text{iso}}$, with the major population narrowly clustered at $\sim 2.3 \times 10^{40}\, {\rm erg}$. The minor population have a characteristic energy of $\sim 1.6 \times 10^{39}$ erg and span approximately one order of magnitude. The distribution hints a near-uniform energy release mechanism for the dominant population as expected from some catastrophic channels, whereas the lower energy component (potentially including repeat-capable sources) may reflect a broader diversity in FRB origins, emission mechanisms, and evolutionary stages.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".