What I typically do for XANES is divide mu-mu_pre_edge_line by a linear
function which goes through the post-edge oscillations.
This division goes over the whole data range, including pre-edge. If the data
has obvious curvature in the post-edge, I'll use a higher-order
polynomial. For transmission data, what sometimes linearizes the background is
to change the abscissa to 1/E^2.7 (the rule-of-thumb absorption
shape) and change it back afterward. All this is, of course, highly subjective
and one of the reasons for taking extended XANES data (300eV,
for instance). For short-range XANES, there isn't enough info to do more than
divide by a constant. Once this is done, my LCF programs allow
a slope adjustment as a free parameter, thus muNorm(E) =
(1+a*(E-E0))*Sum_on_ref{x[ref]*muNorm[ref](E)}. A sign that this degree of
freedom
may be being abused is if the sum of the x[ref] is far from 1 or if a*(Emax-E0)
is large. Don't get me started on overabsorption :-)
mam
On 5/15/2013 7:35 AM, Matt Newville wrote:
Hi Folks,
Over on the github pages for larch, Mauro and Bruce raised an issue
about the "flattening" in Athena. See
https://github.com/xraypy/xraylarch/issues/44
I've added a "flattened output" from Larch's pre_edge() function, but
the question has been raised of whether this is "better" than the
simpler normalized spectra, especially for doing PCA and/or LCF for
XANES.
Currently, the "normalized" spectra is just "(mu -
pre_edge_line)/edge_step". Clearly, a line fitted to the pre-edge of
the spectra is not sufficient to remove all instrumental backgrounds.
In some sense, flattening attempts to do a better job, fitting the
post-edge spectra to a quadratic function. As Mauro, Bruce, and
Carmelo have pointed out, it is less clear that it is actually better
for XANES analysis. I think the main concerns are that a) it is so
spectra-specific, and b) it turns on at E0 with a step function.
Bruce suggested doing something more like MBACK or Ifeffit's bkg_cl().
It would certainly be possible to do some sort of "flattening" so
that mu follows the expected energy dependence from tabularized mu(E).
Does anyone else have suggestions, opinions, etc? Feel free to give
them here or at the github page....
--Matt
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