Searching for the spectral depolarization of ASKAP one-off FRB sources
Bibliographic record
Abstract
ABSTRACT Fast Radio Bursts (FRBs) are extragalactic transients of (sub-)millisecond duration that show wide-ranging spectral, temporal, and polarimetric properties. The polarimetric analysis of FRBs can be used to probe intervening media, study the emission mechanism, and test possible progenitor models. In particular, low-frequency depolarization of FRBs can identify dense, turbulent, magnetized, ionized plasma thought to be near the FRB progenitor. An ensemble of repeating FRBs has shown low-frequency depolarization. The depolarization is quantified by the parameter $\sigma _{\rm RM}\,$, which correlates with proxies for both the turbulence and mean magnetic field strength of the putative plasma. However, while many non-repeating FRBs show comparable scattering (and hence inferred turbulence) to repeating FRBs, it is unclear whether their surrounding environments are comparable to those of repeating FRBs. To test this, we analyse the spectro-polarimetric properties of five one-off FRBs and one repeating FRB, detected and localized by the Australian Square Kilometer Array Pathfinder. We search for evidence of depolarization due to $\sigma _{\rm RM}\,$ and consider models where the depolarization is intrinsic to the source. We find no evidence (for or against) the sample showing spectral depolarization. Under the assumption that FRBs have multipath propagation-induced depolarization, the correlation between our constraint on $\sigma _{\rm RM}\,$ and RM is consistent with repeating FRBs only if the values of $\sigma _{\rm RM}\,$ are much smaller than our upper limits. Additionally, the correlation between the constraints on $\sigma _{\rm RM}\,$ and τs is inconsistent with repeating FRBs. The observations provide further evidence for differences in the typical environments and sources of one-off and repeating FRBs.
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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 | 0.002 | 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 | 0.000 |
| 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".