The Southern Twenty-centimetre All-sky Polarization Survey (STAPS): Survey description and maps
Bibliographic record
Abstract
We present data processing and verification of the Southern Twenty-centimetre All-sky Polarization Survey (STAPS) conducted with Murriyang, the Parkes 64-m telescope. The survey covers the sky area of −89° < Dec < 0° and the frequency range of 1.3–1.8 GHz split into 1-MHz channels. STAPS was observed commensally with the S-band Polarization All-Sky Survey (S-PASS). The survey is composed of long azimuth scans, which allows us to absolutely calibrate Stokes Q and U with the data processing procedure developed for S-PASS. We obtained I, Q, and U maps on both the flux density scale (Jy beam−1) and the main beam brightness temperature scale (K), for the 301 frequency channels with sufficiently good data. The temperature scale is tied to the Global Magneto-ionic Medium Survey (GMIMS) high-band north sky survey conducted with the Dominion Radio Astrophysical Observatory 26-m telescope. All the STAPS maps are smoothed to a common resolution of 20′. The root mean square (rms) noise per channel ranges from about 16 mK to 8 mK for I, and from about 8 mK to 5 mK for Q and U, at frequencies from 1.3 to 1.8 GHz. The rms noise in Q and U varies with declination and reaches minimum at declination of −89°. We also ran rotation measure (RM) synthesis and RM clean to obtain peak polarized intensity and Faraday depth maps. The whole STAPS data processing was validated by comparing flux densities of compact sources, pixel flux density versus pixel flux density for Cen A, pixel temperature versus pixel temperature for the entire survey area, and the RMs of extragalactic sources between STAPS and other measurements. The uncertainty of the flux density scale is less than 10%. STAPS delivers an L-band (λ20 cm) multifrequency polarization view of the Galaxy, and will help advance our understanding of the Galactic magnetic field and magnetized interstellar medium.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".