A comparison of interferometric and single-dish methods to measure distances to pulsar scattering screens
Bibliographic record
Abstract
Abstract Investigations of small-scale structure in the interstellar medium through pulsar scintillation and the use of scattering screens as interferometers to resolve pulsar emission regions rely on reconstructions of the spatial distributions of scattered pulsar flux and the distances to scattering screens. In many cases, a single, highly anisotropic scattering screen is responsible for pulsar scintillation. This leads to a sparsity of information in the secondary spectrum which can be leveraged to measure the scattering geometry and map the distribution of scattered images of the pulsar using Very Long Baseline Interferometry. It is also possible to make these same measurements using only the autocorrelations from simultaneous observations at multiple stations. We compare the results from these two methods applied to PSR B0834+06, and find that the two main measurables, the effective distance and velocity, obtained from these two analyses agree to within ∼10 per cent. The relative ease of using autocorrelations from simultaneous single-dish measurements, where the data rates are much lower, only millisecond clock precision is required and the results are less sensitive to the calibration of the data, means that this method opens up a more observationally accessible route for mapping pulsar scattering screens. Since this method does not require the recording and storage of baseband data, it can be applied to ultrawide-band observations, which typically have unmanageable baseband data rates but are vital for testing theoretical models of pulsar scintillation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 0.001 |
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".