Bias-corrected Fast Radio Burst Population and Spectra Using CHIME Injection Data
Bibliographic record
Abstract
Abstract Fast radio bursts (FRBs), a class of millisecond-scale, highly energetic phenomena with unknown progenitors and radiation mechanisms, require proper statistical analysis as a key method for uncovering their mysteries. In this research, we build on the bias correction method using pulse injections for the first Canadian Hydrogen Intensity Mapping Experiment/FRB catalog, to include correlations between properties and to analyze the FRB population spectrum. This model includes six FRB properties: dispersion measure (DM), pulse width, scattering timescale, spectral index, spectral running, and fluence. By applying the multidimensional weight function calculated by the model, we update the corrected distributions, suggesting that more low-DM, short- and long-width, and short-scattering timescale events may exist. Using one-off events and the first bursts from repeaters, the derived intrinsic population spectrum has a best-fit power law of F ( ν ) ∝ ν α , where α = −2.29 ± 0.29. This confirms previous indications that FRBs are brighter or more numerous at low frequencies. Analyzing nonrepeaters only, we find α = −2.50 ± 0.43, while including all bursts from repeaters produces α = −1.91 ± 0.20. This hints that active repeaters, low-rate repeaters, and nonrepeaters may have different progenitors, mechanisms, or evolutionary stages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 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".