A broad-band spectro-polarimetric view of the NVSS rotation measure catalogue – I. Breaking the nπ-ambiguity
Bibliographic record
Abstract
The NRAO VLA Sky Survey (NVSS) Rotation Measure (RM) catalogue is invaluable for the study of cosmic magnetism. However, the RM values reported in it can be affected by n$\pi$-ambiguity, resulting in deviations of the reported RM from the true values by multiples of +-652.9 rad m-2. We therefore set off to observationally constrain the fraction of sources in the RM catalogue affected by this ambiguity. New broadband spectro-polarimetric observations were performed with the Karl G. Jansky Very Large Array (VLA) at 1--2 GHz, with 23 n$\pi$-ambiguity candidates selected by their peculiarly high |RM| values. We identified nine sources with erroneous RM values due to n$\pi$-ambiguity and 11 with reliable RM values. In addition, we found two sources to be unpolarised and one source to be inconsistent with neither n$\pi$-ambiguity nor reliable RM cases. By comparing the statistical distributions of the above two main classes, we devised a measure of how much a source's RM deviates from that of its neighbours: $\Delta/\sigma$, which we found to be a good diagnostic of n$\pi$-ambiguity. With this, we estimate that there are at least 50 sources affected by n$\pi$-ambiguity among the 37,543 sources in the catalogue. Finally, we explored the Faraday complexities of our sources revealed by our broadband observations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".