Revealing the Dynamic Magnetoionic Environments of Repeating Fast Radio Burst Sources through Multiyear Polarimetric Monitoring with CHIME/FRB
Bibliographic record
Abstract
Abstract Fast radio bursts (FRBs) display a confounding variety of burst properties and host-galaxy associations. Repeating FRBs offer insight into the FRB population by enabling spectral, temporal, and polarimetric properties to be tracked over time. Here, we report on the polarized observations of 12 repeating sources using multiyear monitoring with the Canadian Hydrogen Intensity Mapping Experiment (CHIME) over 400–800 MHz. We observe significant rotation measure (RM) variations from many sources in our sample, including RM changes of several hundred radians per square meter over month timescales from FRBs 20181119A, 20190303A, and 20190417A, and more modest RM variability (ΔRM ≲ few tens of radians per square meter) from FRBs 20181030A, 20190208A, 20190213B, and 20190117A over equivalent timescales. Several repeaters display a frequency-dependent degree of linear polarization that is consistent with depolarization via scattering. Combining our measurements of RM variations with equivalent constraints on DM variability, we estimate the average line-of-sight magnetic field strength in the local environment of each repeater. In general, repeating FRBs display RM variations that are more prevalent and/or extreme than those seen from radio pulsars in the Milky Way and the Magellanic Clouds, suggesting repeating FRBs and pulsars occupy distinct magnetoionic environments.
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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 | 0.000 |
| 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 | 0.000 | 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".