Constraining the Faint-end Slope of the Fast Radio Burst Energy Function Using CHIME/FRB Catalog-1 and Local Volume Galaxies
Bibliographic record
Abstract
Abstract Despite hundreds of detected fast radio bursts (FRBs), the faint-end slope (γ) of their energy distribution remains poorly constrained, hindering understanding of whether bright, cosmological FRBs and faint, Galactic magnetar SGR 1935+2154-like bursts share a common origin. In this study, we constrain this faint-end slope, modeled with a Schechter-like distribution, by searching for potential associations between bursts from the CHIME/FRB Catalog-1 and galaxies in the Local Volume. We crossmatched CHIME/FRB Catalog-1 FRBs with 495 Local Volume galaxies within 21 Mpc, identified from the HECATE catalog, and found no associations. Assuming the FRB energy function extends to ∼3 × 1034 erg—the energy of the Galactic magnetar burst from SGR 1935+2154—this null result constrains γ to be <2.3 (95% confidence upper limit), representing the first empirical estimate for extragalactic FRBs at such low energies. This finding supports the hypothesis that the FRB population is dominated by bright, likely cosmological bursts with a relatively flat energy distribution (γ < 2.5). However, the constraint weakens if higher-energy thresholds are assumed. A flatter energy function is consistent with the observed anticorrelation between FRB dispersion measure and fluence, as seen across various observational bands. While the contribution of low-energy bursts, such as those from the Galactic magnetar SGR 1935+2154, appears minimal, our results suggest that normal magnetars like SGR 1935+2154 could dominate the FRB population if their burst rates and energies scale with age and magnetic field. The upcoming CHIME/FRB Catalog-2 data and targeted nearby galaxy surveys will further refine these constraints, offering critical insight into whether FRBs arise from a single population or diverse origins.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".