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The spectrum ratio is treated as a waveform, and that waveform is analyzed with a Wiener filter in the "time" domain.  It is actually a matched filter where the noise is characterized by taking the ratio of non-exposed spectra from different images - should be flat but the instability of the reference optical spectrum causes it to have correlated movements.  So, the procedure for producing those matched filter weights goes like this:

1. Collect several non-xray exposed spectra;
2. for each pair make a ratio waveform;
3. calculate the auto-correlation function of those waveforms (average several of them);
4. Collect your best averaged ratio with signal;
5. smooth out any non-physics features

There's a script (http://pswww.slac.stanford.edu/svn-readonly/psdmrepo/TimeTool/trunk/data/timetool_setup.py) which shows the last steps of the calibration process that produces the matched filter weights from the autocorrelation function and the signal waveform.  That script has some inputs hard-coded into it, but it at least shows the procedure.  It requires some manual intervention to get a sensible answer, since there are often undesirable features in the signal that the algorithm picks up and tries to optimize towards.