Comprehensive analysis of the Apertif fast radio burst sample
Bibliographic record
Abstract
Understanding the origin of energetic fast radio bursts (FRBs) has become the main science driver of recent dedicated FRB surveys powered by real-time instrumentation. Between July 2019 and February 2022, we carried out ALERT, an FRB survey at 1370 MHz using the Apertif Radio Transient System (ARTS) installed at the Westerbork Synthesis Radio Telescope (WSRT). Here we report the detection of 18 new FRBs. We studied the properties of the entire 24-burst sample that were detected during the survey. For five bursts, we identified host galaxy candidates within their error regions with > 50% probability association. We observed an average linear polarisation fraction of ∼43% and an average circular polarisation fraction consistent with 0%. One-third of the FRBs display multiple components. These burst structures and the polarisation fractions are strikingly similar to those observed in young energetic pulsars and magnetars. The Apertif FRBs next reveal a population of highly scattered bursts. Given the observing frequency and time resolution, the scattering of most FRBs is likely to have been produced in the immediate circumburst environment. Furthermore, two FRBs show evidence of high rotation measure values, which could reach |RM|> 10 3 rad m −2 in the source reference frames. This corroborates that some source environments are dominated by magneto-ionic effects. Together, the scattering and rotation measures that ALERT has found prove that a large fraction of FRBs are embedded in complex media such as star-forming regions or supernova remnants. Through the discovery of FRB 20200719A, the third most dispersed FRB so far, we further show that one-off FRBs emit at frequencies in excess of 6 GHz, the highest known to date. We compare this to the radio-bright high-frequency emission seen in magnetars. Finally, we determine an FRB all-sky rate of 459 −155 +208 sky −1 day −1 above a fluence limit of 4.1 Jy ms, and a fluence cumulative distribution with a power-law index γ = −1.23 ± 0.06 ± 0.2, which is roughly consistent with the Euclidean Universe predictions. Through the high resolution in time, frequency, polarisation, and localisation that ALERT featured, we were able to determine the morphological complexity, polarisation, local scattering and magnetic environment, and high-frequency luminosity of FRBs. We find all of these parameters strongly resemble those seen in young, energetic, highly magnetised neutron stars.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".