Different local kernels

The problem: Not all residual spectrum outliers originate solely from line strength mismatches. In general, line width mismatches or line center shifts will be present in the residual spectrum as non Gaussian correlated structures. Small mismatches can mostly be absorbed by the global kernel. But large mismatches will have conspicuous p-Cygni type profiles, or double peaked profiles.

Suggested solution: As noted in Czekala et al. 2015 and elsewhere, different local kernels can be designed and instantiated.

Practical Considerations and Costs:
How will these local kernels be instantiated? Can the instatiation be done automatically? Right now the local kernels are placed with a sigma clipping strategy. Such a strategy alone would not be able to distinguish different kernels.

No files, tests, or entry points are named. Start by locating the existing local-kernel placement and sigma-clipping implementation, then read how residual spectra and correlated structures are represented. Done would require a decided and documented approach for instantiating distinct kernels, including whether placement can be automated and how line-width or line-center mismatches are distinguished.