[Ifeffit] XANES fitting

[Matt Newville]

Hi Riti,

I hope it's OK that I'm sending this to the ifeffit mailing list, as
there might be other interesting perspectives on your questions.

On Tue, Oct 11, 2011 at 3:47 PM, Ritimukta Sarangi <ritis at stanford.edu> wrote:
> Hi Matt,
> This is Riti again. This time I have a question about EXAFS, which you are an expert on :-) I would like to get your view on
> systematic errors in EXAFS. Say the data were collected on a standard EXAFS beamline like 7-3 at SSRL. The data have
> been collected using fluorescence mode and a 30 element Ge detector. The data are modest, between k=2-15 A.
>
> I used to quote in my papers that typical statistical and systematic errors lead to a total error in first shell distances of
>  +-0.02 A.  I know it can be more than that in some circumstances but then I tried to look at it a little more closely to
> separate out the systematic error from all sources (instrument, measurement, detector, Analysis), but I could not find a
> good reference. I understand that this is a difficult number to estimate. I was hoping that you could shed some light on it.
> Thanks!
> -Riti
>

I'm not sure of a good general reference for that either, though
perhaps some of the papers from Corwin Booth are a good place to
start?   Quoting +/- 0.02 Ang seems like a reasonable estimate for a
typical value, but that's hardly rigorous.

My sense is that it's pretty challenging to separate "systematic" and
"random" noise for most data sets.  Fluorescence data from a
solid-state detector is probably the best-case for such a separation,
because you have multiple detector elements or repeated scans,  and
usually the individual detectors or scans really are dominated by
statistical fluctuations.   If you add detector elements or scans
together until the noise in the data (or variations in estimated
parameters or estimated error bars)   stops getting better,
systematic errors are starting to be noticeable. Eventually, as you
increase the fluorescence counts, the fits will stop improving, which
should be when systematic errors dominate.

--Matt