Disentangling interstellar plasma screens with pulsar VLBI: combining auto- and cross-correlations
Bibliographic record
Abstract
Abstract Current methods of measuring distances to pulsar scattering screens rely on a single screen dominating the scintillation pattern. We present a novel technique to reconstruct the scattered flux of a pulsar and solve for the scattering geometry in cases where the scattering environment along the line of sight to the pulsar is complex and may be composed of multiple scattering screens. This technique combines interferometric visibilities with cross-correlations of single-station intensities. It takes advantage of the fact that if one considers the interference of radiation from two points in the scattered image in delay–delay rate space, the visibilities are sensitive to the mean angular position of the points, while the cross-correlated intensities are sensitive to their angular separation. By combining the visibilities and the cross-correlated intensities, it is possible to measure the angular locations of both points in the pair. We show that this technique is able to reconstruct the published scattering geometry of PSR B0834+06. We then apply this technique to one-dimensional simulations of more complicated scattering systems, where we find that it can distinguish features from different scattering screens. This technique holds promise for studies of the interstellar medium and pulsars themselves: It will allow the application of scintillometry techniques, such as resolving pulsar emission regions using interstellar scattering, to sources for which a current lack of understanding of the scattering environment has precluded their use.
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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 | 0.002 |
| 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 | 0.001 | 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".