I had a great (weekly) call with Andy Casey (Monash) today. We discussed many things, including a hilarious idea for April Fools' that Matt Daunt (NYU) and I conceived. But one of my action items out of the meeting is to start a short note on how to avoid ever shifting and stacking your data (think spectra taken at different times of year, so at different Doppler shifts relative to the Earth). How can you combine data without doing any interpolation of the data? The answer is simple: You forward-model your data with a model that (by optimization) becomes the average of those data. (And the optimization is often closed-form.) Then you only ever shift the model, and you don't have to deal with shifting noise arrays or mask arrays, or anything else. I'll try to write a stub this weekend.
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