Parkes observations for project P1238 semester 2024OCTS_05
Bibliographic record
Abstract
Young and energetic pulsars can power strong pulsar wind nebula (PWN), which can be observed from radio to X-ray and beyond. Leveraging the sensitivity and high-resolution of next-generation radio surveys like ASKAP EMU and SARAO MeerKAT 1.3 GHz Galactic Plane Survey (SMGPS), we are now able to identify pulsar candidates associated with PWNe and SNRs. We can then use these PWN and SNR associations to guide our search of radio pulsars. This strategy was demonstrated successful by our recent discoveries of two high dispersion measure (DM) pulsars powering a bow-shocked PWNe using ASKAP EMU radio continuum images. Recently, we analysed several SMGPS fields and identified seven PWNe candidates potentially associated to three new and four known Galactic SNRs. Here we propose to use the Parkes UWL receiver to carry out the targeted search of radio pulsars powering these PWNe. Since these SNRs are located in Galactic plane and can be distant, we expect them to have large DMs. Such high DMs will lead to strong scattering and smearing and make pulsars undetectable in previous pulsar surveys. The wide frequency coverage of UWL, especially the high frequencies, will allow us to avoid these effects and therefore offer us a better chance to find these pulsars.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.089 |
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".