[Ifeffit] question about S02 (passive electron reduction factor)

[Bruce Ravel]

Hi,

Yesterday we had a question about oddly small S02 values.  In his
thorough summary of possible explanations, Scott mentioned this:

On Wednesday 25 February 2004 01:18 pm, Scott Calvin wrote:
> Another is normalization. If you're using Athena for background
> subtraction make sure you look at the pre-edge line and post-edge
> curve and see that they look reasonable. Sometimes if the background
> has a funny shape to it Athena can create a post-edge curve that
> shoots way up at the edge energy; this could also lead to the effect
> you're describing.

I thought that a word of explanation about why Athena does what she
does might be helpful to folks.  In this email I am going to refer to
data taken on an iron foil.  To follow along, fire up Athena and
select "Import a demo project" from the Help menu.  Select the
`calibrate.prj' file.  This contains an iron foil spectrum.

Set the plotting range in energy to [-200:1500].  Set the upper bound
of the normalization range to 400.  Click on the "post-edge line"
button in the plotting options section.  This will plot the data, the
background, and the post-edge line.  Note that the post-edge line is
U-saped, diverging from the data a bit at low energy and significantly
at high energy.  Note also that the edge step is about 3.05.

Now set the upper bound of the normalization range to 1700 and click
the red E plotting button.  Now the post-edge line goes through the
data and the edge step is about 2.86.

So what's going on?  Well, the post-edge line is determined by
regressing a quadratic polynomial to the data in the user-specified
normalization range.  When the upper bound was set to 400, the square
term in the polynomial was quite large because *that* was the
regression to the data over that short data range.  When the upper
bound was set to 1700, the square term was a lot smaller, the
post-edge line was closer to linear and it was constrained by the
regression to follow the data over the entire data range.

Setting the upper bound of the normalization range had the effect of
increasing the edge step by about 6%.  Remember that the edge step is
the value of the post-edge line extrapolated back to E0 after the
pre-edge line is removed from the data.  With the short energy range,
the edge step is too large and the chi(k) is attenuated.  In the
subsequent fit, S02 will have to be similarly smaller to compensate.
In this case it was a 6% effect, but if the post-edge line is really
screwy, the attenuation could be much larger.

So this was the long-winded way of repeating what Scott said -- plot
the post-edge line and make sure the regression was done such that the
post-edge line goes through the entire data in a way that seems
sensible.  It is also, as Scott said, prudent to check the pre-edge
line as well.

B



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