Interpolation of rotation measure data with different kernels
Interpolation of RM data helps us fill in the gaps between sparse RM measurements. Previous work has been done in the interpolation of galactic foreground RM, culminating in the Galactic Faraday rotation sky produced by Hutschenreuter et al. (2022). This projects focuses on the interpolation of foreground RM data, using simulation cubes from Seta & Federrath (2021), using a patchy RM sky and a filamentary RM sky to test the kernels in two broad categories of RM structures that are found in the sky. The interpolation techniques tested are: Inverse Distance Weighting (IDW), Natural Neighbour Interpolation (NNI), Inverse Multiquadric (IM), Thin-Plate Splines (TPS), and the Bayesian Rotation Measure Sky (BRMS). The BRMS code present in this repository is adapted from the original implementation of Hutschenreuter et al. (2022).
All code necessary to produce the figures and results from Khadir et al. 2024 are included in the notebook that can be found here.
It must be noted that these interpolation techniques can also be used for any other 2D astronomical data.