Hi Wojciech,
My question is the following: does anyone of you have some experience with such procedure? And if yes, shall than distinguish between the first shell of nearest neighbors and the rest of the atoms in terms of their E0 corrections (using 2 parameters)? Or perhaps one can use separate E0's for each path?
I'd say it's not usual to need more than 1 E0 parameter. In some cases, two distinct E0's (that is, outside their error bars) are helpful in a fit. I'm not sure there is a sensible way of predicting which cases these would be, but it seems to have been needed most clearly in solids with fairly ionic bonds. And yes, it generally ends up that using one E0 for the first shell and another E0 for the rest of the paths is what ends up with distinct values. I don't recall a case of needing more than two E0's, probably because the error bars just get too large.
The second question concerns the background correction in Artemis: I was trying to find an easy explanation of what this procedure does during the fit but I guess I'm still somewhat confused how one can judge whether the spline co-refinement does the right job?
Sadly, I'm not sure how to judge whether it does a good job other than by visual inspection. The refinement method is pretty simple, with only a few additional ingredients from a 'normal fit'. First, instead of modeling the measured XAFS chi(k) as Sum_of_Feff_Paths, it models as chi_model(k) = Spline(k) + Sum_of_Feff_Paths(k) The Spline(k) here (written as the *.kbkg array after the fit is done) has adjustable parameters (y-values through which the curve must pass) spaced evenly in k. The number of spline parameters is N_spline = 1 + 2*(kmax-kmin)*rmin / pi The fit is then not between R=[rmin,rmax], but between R=[0,rmax] The idea is that everything below rmin is 'background' and above rmin is 'data'. In that way, rmin acts like Autobk's Rbkg. In principle, the structural parameters should only affect the model above Rmin, and the spline parameters only affect the model below Rmin. An important point is that because all the parameters are refined together, we can determine their correlations: how does changing the background affect E0, deltaR, N, etc, and have these correlations included in the estimate of the uncertainties.
Some people on this mailing list underline that one should avoid high correlations between fitting parameters and background parameters in order to make sure that the fit is correct. But is it the only criterion?
You can't avoid the high correlation between E0 and deltaR or between N*S02 and sigma2. I'd say that it's not that you want to avoid high correlations, but that you want to understand their impact on the analysis. They definitely alter the uncertainties in the fitted parameters. The better criterion for whether a fit is believable ("making sure the fit is correct" is a pretty high standard!!) is whether chi_square is small, whether the model is reasonable, and whether the adjusted parameters have some physical meaning and believable values and uncertainties. Sorry I can't be more specific than that.
In some of my fits background line looks kind of constant over the entire R-range which was taken into the fit. Sometimes it becomes like a modulation peaking underneath some scattering paths' even if the background parameters don't correlate significantly with fitting parameters I have my doubts whether the result is trustful. Any hints about this procedure?
I'm not sure I understand you here. The background looks constant in R-space?? That does seem strange. Can you give more details? Hope that helps or is at least a start.... --Matt