In the anecdotal category, I've seen some fairly bizarre high-r
behavior on beamline X23B at the NSLS, which I tentatively attribute
to feedback problems. That line, as many of you know, can be a little
pathological at times. I've also collected some data to examine this
issue on X11A, a more conventional beamline, but have never gotten
around to looking at it--I hope to soon.
In any case, I don't really understand the logic of averaging
scans based on some estimate of the noise. For that to be appropriate,
you'd have to believe there was some systematic difference in the
noise between scans. What's causing that difference, if they're
collected on the same beamline on the same sample? (Or did I
misunderstand Michel's comment--was he talking about averaging data
from different beamlines or something?) If there is no systematic
changes during data collection, then the noise level should be the
same, and any attempt to weight by some proxy for the actual
noise will actually decrease the statistical content of the averaged
data by overweighting some scans (i. e. random fluctuations in the
quantity being used to estimate the uncertainty will cause some scans
to dominate the average more heavily, which is not ideal if the actual
noise level is the same). If, on the other hand, there is a systematic
difference between subsequent scans, it is fairly unlikely to be
"white," and thus will not be addressed by this scheme
anyway. Perhaps one of you can give me examples where this kind of
variation in data quality is found.
So right now I don't see the benefit to this method. Particularly
if it's automated, I hesitate to add hidden complexity to my data
reduction without a clear rationale for it.
--Scott Calvin
Naval Research Lab
Code 6344
Matt Newville wrote:
I'd assumed that vibrations would
actually cause fairly white noise,
though feedback mechanisms could skew towards high frequency.
Other
effects (temperature/pressure/flow fluctuations in ion chamber
gases
and optics) might skew toward low-frequency noises. I have not
seen
many studies of vibrations, feedback mechanism, or other
beamline-specific effects on data quality, and none discussing the
spectral weight of the beamline-specific noise.
On the other hand, all data interpolation schemes do some
smoothing,
which suppresses high frequency components. And it usually
appears
that the high-frequency estimate of the
noise from chi_noise() or
Feffit gives an estimate that is significantly *low*.
Anyway, I think using the 'epsilon_k' that chi_noise() estimates
as
the noise in chi(k) is a fine way to do a weighted averages of
data.
It's not perfect, but neither is anything
else.
--Matt
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