Interpolation Techniques for Reconstructing Galactic Faraday Rotation
Bibliographic record
Abstract
Abstract The line-of-sight structure of the Galactic magnetic field (GMF) can be studied using Faraday rotation measure (RM) grids. We analyze how the choice of interpolation kernel can affect the accuracy and reliability of reconstructed RM maps. We test the following kernels: inverse distance weighting (IDW), natural neighbor interpolation (NNI), inverse multiquadric interpolation (IM), thin-plate spline interpolation (TPS), and a Bayesian rotation measure sky (BRMS); all techniques were tested on two simulated Galactic foreground RMs (one assuming the GMF has patchy structures and the other assuming it has filamentary structures) using magnetohydrodynamic simulations. Both foregrounds were sampled to form RM grids with densities of ∼40 sources deg −2 and area ∼144 deg 2 . The techniques were tested on data sets with different noise levels and Gaussian random extragalactic RM contributions. The data set that most closely emulates expected data from current surveys, such as the POlarization Sky Survey of the Universe’s Magnetism (POSSUM), had extragalactic contributions and a noise standard deviation of ∼1.5 rad m −2 . For this data set, the accuracy of the techniques for the patchy structures is ranked from best to worst as BRMS, NNI, TPS, IDW, and IM; in the filamentary simulated foreground, the ordering is BRMS, NNI, TPS, and IDW. IDW is the most computationally expensive technique, while TPS and IM are the least expensive. BRMS and NNI have the same, intermediate computational cost. This analysis lays the groundwork for Galactic RM studies with large radio polarization sky surveys, such as POSSUM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".