A MILLISECOND INTERFEROMETRIC SEARCH FOR FAST RADIO BURSTS WITH THE VERY LARGE ARRAY
Bibliographic record
Abstract
We report on the first millisecond timescale radio interferometric search for the new class of transient known as fast radio bursts (FRBs). We used the Very Large Array (VLA) for a 166 hr, millisecond imaging campaign to detect and precisely localize an FRB. We observed at 1.4 GHz and produced visibilities with 5 ms time resolution over 256 MHz of bandwidth. Dedispersed images were searched for transients with dispersion measures from 0 to 3000 pc cm −3 . No transients were detected in observations of high Galactic latitude fields taken from 2013 September though 2014 October. Observations of a known pulsar show that images typically had a thermal-noise limited sensitivity of 120 mJy beam −1 ( ; Stokes I) in 5 ms and could detect and localize transients over a wide field of view. Our nondetection limits the FRB rate to less than sky −1 day −1 (95% confidence) above a fluence limit of 1.5 Jy ms. The VLA rate limit is consistent with past estimates when published flux limits are recalculated with a homogeneous definition that includes effects of primary beam attenuation, dispersion, pulse width, and sky brightness. This calculation revises the FRB rate downward by a factor of 2, giving the VLA observations a roughly 50% chance of detecting a typical FRB, assuming a pulse width of 3 ms. A 95% confidence constraint would require 600 hr of similar VLA observing. Our survey also limits the repetition rate of an FRB to 2 times less than any known repeating millisecond radio transient.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".