No Low-frequency Emission from Extremely Bright Fast Radio Bursts
Bibliographic record
Abstract
Abstract We present the results of a coordinated campaign conducted with the Murchison Widefield Array (MWA) to shadow fast radio bursts (FRBs) detected by the Australian Square Kilometre Array Pathfinder (ASKAP) at 1.4 GHz, which resulted in simultaneous MWA observations of seven ASKAP FRBs. We de-dispersed the 24 × 1.28 MHz MWA images across the 170–200 MHz band taken at 0.5 s time resolution at the known dispersion measures (DMs) and arrival times of the bursts and searched both within the ASKAP error regions (typically ∼10′ × 10′), and beyond (4° × 4°). We identified no candidates exceeding a 5σ threshold at these DMs in the dynamic spectra. These limits are inconsistent with the mean fluence scaling of α = −1.8 ± 0.3 ( , where ν is the observing frequency) that is reported for ASKAP events, most notably for the three high-fluence ( Jy ms) FRBs 171020, 180110, and 180324. Our limits show that pulse broadening alone cannot explain our non-detections, and that there must be a spectral turnover at frequencies above 200 MHz. We discuss and constrain parameters of three remaining plausible spectral break mechanisms: free–free absorption, intrinsic spectral turnover of the radiative processes, and magnification of signals at ASKAP frequencies by caustics or scintillation. If free–free absorption were the cause of the spectral turnover, we constrain the thickness of the absorbing medium in terms of the electron temperature, T, to <0.03 (T/104 K)−1.35 pc for FRB 171020.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".