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:
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 <ifeffit-bounces@millenia.cars.aps.anl.gov>
On Behalf Of LIMA DA SILVA, WILL (PGR)
Sent: Tuesday, October 12, 2021 4:10 PM
To: ifeffit@millenia.cars.aps.anl.gov
Subject: [Ifeffit] Fit in R or k
Dear IFEFFIT members,
I have been fitting some EXAFS data, and my supervisor and I are having doubts if it's better to fit in k or R space. What are the main differences between the two approaches?
I have learnt that R space is the right approach, but I am not sure how to explain it.
Much appreciated,
Will Silva