DEGREE OF POLARIZATION AND SOURCE COUNTS OF FAINT RADIO SOURCES FROM STACKING POLARIZED INTENSITY
Bibliographic record
Abstract
We present stacking polarized intensity as a means to study the polarization of sources that are too faint to be detected individually in surveys of polarized radio sources. Stacking offers not only high sensitivity to the median signal of a class of radio sources, but also avoids a detection threshold in polarized intensity, and therefore an arbitrary exclusion of sources with a low percentage of polarization. Correction for polarization bias is done through a Monte Carlo analysis and tested on a simulated survey. We show that the nonlinear relation between the real polarized signal and the detected signal requires knowledge of the shape of the distribution of fractional polarization, which we constrain using the ratio of the upper quartile to the lower quartile of the distribution of stacked polarized intensities. Stacking polarized intensity for NRAO VLA Sky Survey (NVSS) sources down to the detection limit in Stokes I , we find a gradual increase in median fractional polarization that is consistent with a trend that was noticed before for bright NVSS sources, but is much more gradual than found by previous deep surveys of radio polarization. Consequently, the polarized radio source counts derived from our stacking experiment predict fewer polarized radio sources for future surveys with the Square Kilometre Array and its pathfinders.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".