I’ll add to Bruce’s comment that if the choice of kmax has a great effect on the parameters found by the fit, that’s an indication of a problem! Good fits should be stable to modest changes in kmax (e.g., an inverse angstrom or two one way or another). The statistics may suggest one kmax or another is somewhat better, but the fitted parameters should not be drifting outside of the ranges defined by their uncertainties. If they are, you have an unstable fit. In that case, there are several possibilities: perhaps you are including an artifact in your k-range (e.g. the beginning of another edge) or a lot of data dominated by noise. Or perhaps your model is having trouble distinguishing between two fitting minima, and is flipping back and forth between them (this is more likely if your fitting model is fairly complicated, with many free parameters). In any case, if you’re fit is highly sensitive to kmax you should investigate to try to determine why.

—Scott Calvin
Sarah Lawrence College

> On Aug 4, 2016, at 11:07 AM, Bruce Ravel <bravel@bnl.gov> wrote:
>
>>
>> 2. The cutting range of Kmax (FT transform parameters) has great effect
>> on FTs of EXAFS, how do I know to use the best value of Kmax;
>
> If you have measured data with signal well above the level of noise, why would you choose to use less data?
>
> Similarly, if, at some point in your data, the signal becomes dominated by noise -- either statistical or systematic -- why would you include it in the analysis?

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