Investigating Extreme Scattering Events by Volumetric Ray-tracing
Bibliographic record
Abstract
Abstract Extreme scattering events (ESEs) are observed as dramatic (>50%) drops in flux density that occur over an extended period of weeks to months. Discrete plasma lensing structures are theorized to scatter the radio waves produced by distant sources such as pulsars, causing the signature decrease in flux density and characteristic caustic spikes in ESE light curves. While plasma lens models in the extant literature have reproduced key features of ESE light curves, they have all faced the problem of being highly overdense and overpressured relative to the surrounding interstellar medium by orders of magnitude. We model ESEs by numerically ray tracing through analytic, volumetric plasma lens models by solving the eikonal equation. Delaunay triangulation connecting the rays approximates the wave front, generating a mapping from the observer plane to the source plane to account for multiple imaging. This eikonal method of ray tracing is tested against known analytic solutions and is then applied to a three-dimensional Gaussian-distributed electron volume density lens and a filament model inspired by Grafton et al. We find convergence of our numerical results with established analytic solutions, validating our numerical method, and reproduce ESE-like light curves. Our numerical ray-tracing method lends itself well to exploring the lensing effects of volumetric turbulence as well as sheet-like lenses, which is currently in progress.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".