Hi Will:
The relationship between the data in k-space and R-space is described in this tutorial:
https://docs.xrayabsorption.org/tutorials/Basics_of_XAFS_to_chi_2009.pdf
Take a look at what a Fourier transform does. In few words I'd say it doesn't matter which representation of the data (k-space or R-space) you model. Both have their good and not so good points.
R-space: You can filter out noise, and higher and/or lower frequencies that you might not be interested in. You can also weight the lower or higher part of the spectra you are fitting by using k-weighting.
k-space: You are working with less processed data and can directly see the noise and high/lower frequency components "on top of" your signal of interest.
If it still isn't clear I'd suggest you start with some of the material at this web site:
https://xrayabsorption.org/tutorials/
There is also this very readable book by Scott Calvin:
https://www.amazon.com/XAFS-Everyone-Scott-Calvin/dp/1439878633/ref=asc_df_1439878633/?tag=hyprod-20&linkCode=df0&hvadid=312091458201&hvpos=&hvnetw=g&hvrand=7369607741759776259&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9021703&hvtargid=pla-455399119345&psc=1&tag=&ref=&adgrpid=63669393113&hvpone=&hvptwo=&hvadid=312091458201&hvpos=&hvnetw=g&hvrand=7369607741759776259&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9021703&hvtargid=pla-455399119345
I also have an EXAFS chapter that I can send you a copy of, if you send me a request directly.
Kind regards,
Shelly
From: Ifeffit