A targeted search for repeating fast radio bursts with the MWA
Bibliographic record
Abstract
ABSTRACT We present a targeted search for low-frequency (144–215 MHz) fast radio burst (FRB) emission from five repeating FRBs using 23.3 h of archival data taken with the Murchison Widefield Array (MWA) voltage capture system (VCS) between 2014 September and 2020 May. This is the first time that the MWA VCS has been used to search for FRB signals from known repeaters, which enables much more sensitive FRB searches than previously performed with the standard MWA correlator mode. We performed a standard single-pulse search with a temporal and spectral resolution of $400\, \mu$s and 10 kHz, respectively, over a $100\, \text{pc}\, \text{cm}^{-3}$ dispersion measure (DM) range centred at the known DM of each studied repeating FRB. No FRBs exceeding a 6σ threshold were detected. The fluence upper limits in the range of 32–1175 and 36–488 Jy ms derived from 10 observations of FRB 20190711A and four observations of FRB 20201124A, respectively, allow us to constrain the spectral indices of their bursts to ≳−1 if these two repeaters were active during the MWA observations. If free–free absorption is responsible for our non-detection, we can constrain the size of the absorbing medium in terms of the electron temperature T to ${\lt} 1.00\times (T/10^4\,\text{K})^{-1.35}\, \text{pc}$, ${\lt} 0.92\times (T/10^4\,\text{K})^{-1.35}\, \text{pc}$, and ${\lt} [0.22\!-\!2.50]\times (T/10^4\,\text{K})^{-1.35}\, \text{pc}$ for FRB 20190117A, FRB 20190711A, and FRB 20201124A, respectively. However, given that the activities of these repeaters are not well characterized, our non-detections could also suggest they were inactive during the MWA observations.
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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 | 0.001 |
| 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".