A method for reconstructing the Galactic magnetic field using dispersion of fast radio bursts and Faraday rotation of radio galaxies
Bibliographic record
Abstract
ABSTRACT With the rapid increase of fast radio burst (FRB) detections within the past few years, there is now a catalogue being developed for all-sky extragalactic dispersion measure (DM) observations in addition to the existing collection of all-sky extragalactic Faraday rotation measurements (RMs) of radio galaxies. We present a method of reconstructing all-sky information of the Galactic magnetic field component parallel to the line of sight, B∥, using simulated observations of the RM and DM along lines of sight to radio galaxies and FRB populations, respectively. This technique is capable of distinguishing between different input Galactic magnetic fields and thermal electron density models. Significant extragalactic contributions to the DM are the predominant impediment in accurately reconstructing the Galactic DM and 〈B∥〉 skies. We look at ways to improve the reconstruction by applying a filtering algorithm on the simulated DM lines of sight and we derive generalized corrections for DM observations at |b| > 10° that help to disentangle Galactic and extragalactic DM contributions. Overall, we are able to reconstruct both large-scale Galactic structure and local features in the Milky Way’s magnetic field from the assumed models. We discuss the application of this technique to future FRB observations and address possible differences between our simulated model and observed data, namely: adjusting the priors of the inference model, an unevenly distributed population of FRBs on the sky, and localized extragalactic DM structures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".