The Effects of Plasma Lensing on the Inferred Dispersion Measures of Fast Radiobursts
Bibliographic record
Abstract
Abstract Radio signals are delayed when propagating through plasma. This type of delay is frequency dependent and is usually used for estimating the projected number density of electrons along the line of sight, called the dispersion measure (DM). The dense and clumpy distribution of plasma can cause refractive deflections of radio signals, analogous to lensing effects. Similar to gravitational lensing, there are two contributions to the time delay effect in plasma lensing: a geometric delay, due to increased path length of the signal, and a dispersive delay due to the change of speed of light in a plasma medium. We show the delay time for two models of the plasma distribution, and point out that the estimated DM can be biased. Since the contribution of the geometric effect can be comparable to that of the dispersive delay, the bias in the measured DM can be dramatically large if plasma lensing effects are not taken into account when signals propagate through a high-density gradient clump of plasma.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".