A Multi-telescope Campaign on FRB 121102: Implications for the FRB Population
Bibliographic record
Abstract
Abstract We present results of the coordinated observing campaign that made the first subarcsecond localization of a fast radio burst, FRB 121102. During this campaign, we made the first simultaneous detection of an FRB burst using multiple telescopes: the VLA at 3 GHz and the Arecibo Observatory at 1.4 GHz. Of the nine bursts detected by the Very Large Array at 3 GHz, four had simultaneous observing coverage at other observatories at frequencies from 70 MHz to 15 GHz. The one multi-observatory detection and three non-detections of bursts seen at 3 GHz confirm earlier results showing that burst spectra are not well modeled by a power law. We find that burst spectra are characterized by a ∼500 MHz envelope and apparent radio energy as high as 10 40 erg. We measure significant changes in the apparent dispersion between bursts that can be attributed to frequency-dependent profiles or some other intrinsic burst structure that adds a systematic error to the estimate of dispersion measure by up to 1%. We use FRB 121102 as a prototype of the FRB class to estimate a volumetric birth rate of FRB sources Mpc −3 yr −1 , where N r is the number of bursts per source over its lifetime. This rate is broadly consistent with models of FRBs from young pulsars or magnetars born in superluminous supernovae or long gamma-ray bursts if the typical FRB repeats on the order of thousands of times during its lifetime.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".