Parkes observations for project P1015 semester 2019APRS_01
Bibliographic record
Abstract
Pulsar scintillation, variation of the observed pulsar flux against time and frequency, contains the information of light path difference between scattered light. Observations of scintillation from many bright pulsars suggest that the scattering is not random, but instead due to highly anisotropic scattering at one more thin lenses along the line of sight. The distance from the lens to the Earth can remain the same for years, and in some cases, the lens produces discrete images of the source that last for a month. The simplicity of the scattered images, and the longevity of the lenses suggest that it may be possible to model the lens with a finite number of parameters, and make a predictive model for pulsar scintillation. We propose to observe the scintillation of the brightest pulsar, B0833-45, which shows evidence of discrete lensed images and highly anisotropic scattering, with the Parkes Ultra Wideband Receiver and the H-OH receiver. With these observations, we will test scintillation models, which make concrete predictions for the frequency evolution of the scintillation pattern. We will also use the polarization data to search for magnetic spatial structures across the lens, which have been theorized as a mechanism to create the lenses responsible for anisotropic scattering. Meanwhile, we will use the great sensitivity and resolution of this giant interstellar lens to constrain the emission region size of the Vela pulsar.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.068 |
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".