Hi Michel, Michel wrote: Finally, another issue - for which I would not lay out my neck, though - is the noise. In EXAFS, the signal to noise increases with k, and of course fitting in the raw k space is another way of dealing conveniently with the noise -or relative uncertainty, as we may name it. However, the FT is a way of filtering out some of this noise, and so maximise the signal from a shell. Likewise, fitting in filtered q-space may yield more accurate results, because some of the noise is filtered out. However, I concur that somehow the fact that the uncertainty on the high-q part of the filtered contribution os greater than in the low q-part should be implemented somehow. It is true that the signal to noise ratio becomes smaller at high k values. It does seem magical that the Fourier transform can pull a signal out of the high k-range where the noise is large and that this signal seems to be well defined. I find it helpful to remember that each signal in the EXAFS chi(k) spectra is given by ONE frequency. That frequency is well defined at low k were the signal to noise ratio is large. Extending this frequency into the high k-region is trivial as long as that frequency co-exists with all the noise at high k. In fact I don't need data at all to extend a well defined frequency to infinity. The part of the signal that is not so well defined at high k is the amplitude. A lot of noise often masks itself by a large variation in amplitude for a given signal at high k, this effect will result in poor values for CN and 2. But the amplitude of the signal has a well known k-dependence so with enough low k information about the amplitude even the amplitude does not become affected by the noise at high k. The point to remember is that the signals are defined from k=0 to k=something bigger than zero. As long as the signal to noise ratio is reasonable from k=0 to some k~10? Then the signal including frequency and amplitude is well described all the way to k=infinity. It is often helpful to look at the fit and the data in k-space all the way out to 16 1/angstroms even if you are only using the k-range to 12 1/angstroms. If the model continues to follow the data to 16 then it is a good model. Shelly Shelly Kelly Bldg 203 RM E113 Skelly@anl.gov Argonne National Laboratory 630-252-7376 9700 S Cass Ave www.mesg.anl.gov Argonne, IL 60440