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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