Starfish

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Starfish currently supports calibration polynomials in the flux domain, parameterized as Chebyshev polynomials (c.f. Czekala et al. 2015). In principle there could be calibration uncertainties attributable to wavelength-domain instrumental artifacts-- basically bad wavelength solution in your data reduction pipeline. The v_z term takes up the zeroth order instrumental artifact, a wavelength shift (of course, in perfectly or near-perfectly calibrated spectra, v_z has astrophysical significance!). However linear or higher-order distortions of your wavelength solutions are possible. In fact, they're not only possible, they're omni-present, but just at a level that may be negligible for most applications.

In any case, if for some reason you are stuck with spectra with poor wavelength calibration, one could straightforwardly implement wavelength-domain calibration polynomials. Such a feature would need to modify the over-sampled wavelength axis in the update_theta() step. This change would add negligibly to the computation time-- the FFT would proceed as normal.

To be clear this is a feature idea enabled by this powerful spectral inference framework, not some inadequacy as might be misconstrued by the GitHub label "Issue", which takes on multiple meanings.

You're bringing up some great points today, Gully. I've thought about this too and have implemented Chebyshev polynomials before to correct for bad wavelength calibration. I didn't care about RVs when I did it, but for people who might care about the v_z parameter from Starfish, the zeroth order component is no longer going to be the v_z of the star. Just something to think about.