Arecibo observations of a burst storm from FRB 20121102A in 2016
Bibliographic record
Abstract
ABSTRACT FRB 20121102A is the first known fast radio burst (FRB) from which repeat bursts were detected, and one of the best-studied FRB sources in the literature. Here we report on the analysis of 478 bursts (333 previously unreported) from FRB 20121102A using the 305-m Arecibo telescope – detected during approximately 59 hours of observations between December 2015 and October 2016. The majority of bursts are from a burst storm around September 2016. This is the earliest available sample of a large number of FRB 20121102A bursts, and it thus provides an anchor point for long-term studies of the source’s evolving properties. We observe that the bursts separate into two groups in the width-bandwidth-energy parameter space, which we refer to as the low-energy bursts (LEBs) and high-energy bursts (HEBs). The LEBs are typically longer duration and narrower bandwidth than the HEBs, reminiscent of the spectro-temporal differences observed between the bursts of repeating and non-repeating FRBs. We fit the cumulative burst rate-energy distribution with a broken power law and find that it flattens out toward higher energies. The sample shows a diverse zoo of burst morphologies. Notably, burst emission seems to be more common at the top than the bottom of our 1150–1730 MHz observing band. We also observe that bursts from the same day appear to be more similar to each other than to those of other days, but this observation requires confirmation. The wait times and burst rates that we measure are consistent with previous studies. We discuss these results, primarily in the context of magnetar models.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".