-----Original Message----- From: Stefano Ciurli [mailto:stefano.ciurli@unibo.it] Sent: Friday, July 23, 2004 6:31 AM To: ravel@phys.washington.edu; XAFS Analysis using Ifeffit Subject: [Ifeffit] weights data processing
Hi folks,
1) when removing background using athena, there is an option of using a k-weight different than 1 (I am NOT talking about k-weight for FT). I noticed that changing this parameter the resulting chi(k) does not change significantly. Actually, going from 1 to 4 as a value for k-weight for Bkgd removal, the only effect I see is a significant reduction of the peak below the first coordination shell in chi(R) while the other features of the spectrum are essentially the same in terms of absolute and relative intensity. A drastic change in both chi(k) and chi(R) is seen if I use k-weight = 5 and above. What is the exact meaning of it? I know what the meaning is in the FT process, but I do not see the reason why I should use a weight different than 1 (let's say 3 for my noisy data) for background removal. How should I behave? Should I take the largest value of k-weight so that the essential features of the chi(k) and chi(R) do not change?
Hi Stefano, I have found a procedure for removing the background that "almost" always works well. It goes something like this and uses different k-weightings in the background removal process. 1) Check out the curvature of the XANES region and how quickly the data goes from zero to 1, call this DExanes. There are in my small opinion two categories for each of these different parameters. Curvature of Xanes can be step like or it can have a large peak. DExanes can be small ~10eV for k-edges of Cu or wide ~30eV for L3-edge of U. 2) Choosing Ezero for the first time: If you have a wide DExanes, then choose Ezero up near the top of the edge. If you have a small DExanes, then choose Ezero around the middle of the edge. 3)Set kweight equal to one or zero (and no theory) and remove the background. This will emphasize the low k-region around the edge, but often get the background for the higher k-region not as well...especially for noisy data. 4)Check out the shape of the background function, the chi(k) data and the FT. Make sure that rbkg=1.0 is a reasonable thing to do. Make sure that you look at the chik data with all three k-weights. Often people say that there is no change in the chik data by changing the kwieght but they are looking at the chik*k3. The change will be greatest at low k, which you don't really see with chik*k3. 5) If you have a large peak in the XANES region, Athena may need help getting the bkg to go through that peak. You have two options. A) increase rbkg so that the spline has more freedom to curve in this region. or B) increase kmin of the spline range, essentially removing the large peak from the data. 6) Fit the first shell of your data, use a well padded chik data range...don't include at least one full oscillation at the beginning. i.e. use kmin=3 or greater. Monitor the value for ezero. If it is greater than 10eV you need to start over. (I have some more details on how to create a theory to be used for bkg removal on my web site http://www.mesg.anl.gov/Skelly.html from the 2004 NSLS EXAFS workshop presentation) 7) Read the theory back into Athena. Now Athena knows what the low k-range should look like, and you can increase the k-weight of the background, use kw=2 or 3, to get the high k-range part of the data correct as well. 8) Fit the data again and use as much of the k-range as possible.
2) In artemis, is it a bug or a wanted feature to have the possibility to activate more than one button for k-weight for the fit and FT? I suppose it is a wanted feature, but then I wonder what is the meaning of using more than one weight for the fit and FT. I am sure some of you can unveil this enigma.
Another couple of reasons to use more than 1 k-weight in the FT of the data: I have found that the uncertainties in the fitted values are about half as big by using all three k-weights, as opposed to using just one k=weight. I have also found that using all three k-weights will force you to model the data much more correctly. When you look at the fits, you need to look at the FT and the chi(k) data using all three k-weights. HTH Shelly