A Nançay Radio Telescope study of the hyperactive repeating FRB 20220912A
Bibliographic record
Abstract
ABSTRACT The repeating fast radio burst source FRB 20220912A was remarkably active in the weeks after its discovery. Here, we report 696 bursts detected with the Nançay Radio Telescope (NRT) as part of the Extragalactic Coherent Light from Astrophysical Transients (ÉCLAT) monitoring campaign. We present 68 observations, conducted from 2022 October to 2023 April, with a total duration of 61 h and an event rate peaking at $75^{+10}_{-9}$ bursts per hour above a fluence threshold of 0.59 Jy ms in the $1.2\!-\!1.7$ GHz band. Most bursts in the sample occur towards the bottom of the observing band. They follow a bimodal wait-time distribution, with peaks at 33.4 ms and 67.0 s. We find a roughly constant dispersion measure (DM) over time ($\delta$DM $\lesssim$ 2 pc cm$^{-3}$) when taking into account ‘sad-trombone’ drift, with a mean drift rate of $-8.8\,$ MHz ms$^{-1}$. None the less, we confirm small $\sim 0.3$ pc cm$^{-3}$ DM variations using microshot structure, while finding that microstructure is rare in our sample–despite the 16 $\mu$s time resolution of the data. The cumulative spectral energy distribution shows more high-energy bursts ($E_\nu \gtrsim 10^{31}$ erg Hz$^{-1}$) than would be expected from a simple power-law distribution. The burst rate per observation appears Poissonian, but the full set of observations is better modelled by a Weibull distribution, showing clustering. We discuss the various observational similarities that FRB 20220912A shares with other (hyper)active repeaters, which as a group are beginning to show a common set of phenomenological traits that provide multiple useful dimensions for their quantitative comparison and modelling.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".