\nThe Galactic Faraday rotation sky 2020
Bibliographic record
Abstract
Aims.This work gives an update to existing reconstructions of the Galactic Faraday rotation sky by processing almost all Faraday rotation data sets available at the end of the year 2020.Observations of extra-Galactic sources in recent years have, among other regions, further illuminated the previously under-constrained southern celestial sky, as well as parts of the inner disc of the Milky Way.This has culminated in an all-sky data set of 55,190 data points, which is a significant expansion on the 41,330 used in previous works, hence making an updated separation of the Galactic component a promising venture.The increased source density allows us to present our results in a resolution of about 1.3•10 -2 deg 2 (46.8 arcmin 2 ), which is a twofold increase compared to previous works.Methods.As for previous Faraday rotation sky reconstructions, this work is based on information field theory, a Bayesian inference scheme for field-like quantities which handles noisy and incomplete data.Results.In contrast to previous reconstructions, we find a significantly thinner and pronounced Galactic disc with small-scale structures exceeding values of several thousand rad m -2 .The improvements can mainly be attributed to the new catalog of Faraday data, but are also supported by advances in correlation structure modeling within numerical information field theory.We furthermore give a detailed discussion on statistical properties of the Faraday rotation sky and investigate correlations to other data sets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.202 | 0.218 |
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".