A probe of the maximum energetics of fast radio bursts through a prolific repeating source
Bibliographic record
Abstract
ABSTRACT Fast radio bursts (FRBs) are sufficiently energetic to be detectable from luminosity distances up to at least seven billion parsecs (redshift $z > 1$). Probing the maximum energies and luminosities of FRBs constrains their emission mechanism and cosmological population. Here, we investigate the maximum energetics of a highly active repeater, FRB 20220912A, using 1500 h of observations. We detect 130 high-energy bursts and find a break in the burst energy distribution, with a flattening of the power-law slope at higher energy – consistent with the behaviour of another highly active repeater, FRB 20201124A. There is a roughly equal split of integrated burst energy between the low- and high-energy regimes. Furthermore, we model the rate of the highest energy bursts and find a turnover at a characteristic spectral energy density of $E^{\rm {char}}_{\nu } = 2.09^{+3.78}_{-1.04}\times 10^{32}$ erg Hz$^{-1}$. This characteristic maximum energy agrees well with observations of apparently one-off FRBs, suggesting a common physical mechanism for their emission. The extreme burst energies push radiation and source models to their limit: at this burst rate a typical magnetar ($B = 10^{15}$ G) would deplete the energy stored in its magnetosphere in $\sim$2150 h, assuming a radio efficiency $\epsilon _\mathrm{radio} = 10^{-5}$. We find that the high-energy bursts ($E_\nu > 3 \times 10^{30}$ erg Hz$^{-1}$) play an important role in exhausting the energy budget of the source.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".