[Ifeffit] amplitude parameter S02 larger than 1

Fri Mar 20 15:28:58 CDT 2015

Yanyun,

The so2 also has a correlation with the Debye factor as well. You should also look at that parameter in the fits you've done. 

Chris

Sent from my iPhone

> On Mar 20, 2015, at 3:46 PM, huyanyun at physics.utoronto.ca wrote:
> 
> Hi Scott,
> 
> Thank you so much for giving me your thought again. It is very helpful to know how you and other XAFS experts deal with unusual situations.
> 
> The floating S02 is fitted to be 1.45+/-0.14, this just means the fit doesn't like the idea of an S02 in a typical range. Instead of setting S02 to 0.9, I have to figure out why it happens and what it might indicate.
> 
> I guess a Hamilton test is done by adjusting one parameter (i.e., S02) while keeping other conditions and model the same. Is that right?  So I record this test as following:
> 
> 1) Floating S02: S02 fits to 1.45+/-0.14, R=0.0055, reduced chi^2=17.86, Percentage=0.53+/-0.04
> 2) Set S02=0.7, R=0.044, reduced chi^2=120.6, percentage=0.81+/-0.2
> 3) set S02=0.8, R=0.030, reduced chi^2=86.10, percentage=0.77+/-0.07
> 3) set S02=0.9, R=0.021, reduced chi^2=60.16, percentage=0.72+/-0.06
> 4) set S02=1.0, R=0.017, reduced chi^2=49.5, percentage=0.67+/-0.05
> 5) set S02=1.1, R=0.012, reduced chi^2=35.1, percentage=0.62+/-0.03
> 6) set S02=1.2, R=0.009, reduced chi^2=24.9, percentage=0.59+/-0.02
> 7) set S02=1.3, R=0.007, reduced chi^2=18.9, percentage=0.57+/-0.02
> 8) set S02=1.4, R=0.0057, reduced chi^2=16.1, percentage=0.55+/-0.02
> 9) Floating S02 to be 1.45+/-0.14
> 10) set S02=1.6, R=0.006, reduced chi^2=17.8, percentage=0.53+/- 0.02
> 11) set S02=2.0, R=0.044, reduced chi^2=120.7, percentage=0.37+/-0.06.
> 
> Therefore, I will say S02 falling in the range 1.2~1.6 gives statistically improved fit, but S02=0.9 is not terrible as well. I agree with you that I could always be confident to say the percentage is 0.64+/-0.15, but I do want to shrink down the uncertainty and think about other possibilities that could cause a large S02.
> 
> I did double-check the data-reduction and normalization process. I don't think I can improve anything in this step. By the way, I have a series of similar samples and their fittings all shows floating S02 larger than one based on the same two-sites model.
> 
> Best,
> Yanyun
> 
> 
> 
> 
> Quoting Scott Calvin <scalvin at sarahlawrence.edu>:
> 
>> Hi Yanyun,
>> 
>> Lots of comments coming in now, so I’m editing this as I write it!
>> 
>> One possibility for why you're getting a high best-fit S02 is that the fit doesn't care all that much about what the value of S02; i.e. there is broad range of S02's compatible with describing the fit as "good." That should be reflected in the uncertainty that Artemis reports. If S02 is 1.50 +/- 0.48, for example, that means the fit isn't all that "sure" what S02 should be. That would mean we could just shrug our shoulders and move on, except that it correlates with a parameter you are interested in (in this case, site occupancy). So in such a case, I think you can cautiously fall back on what might be called a "Bayesian prior"; i.e., the belief that the S02 should be "around" 0.9, and set the S02 to 0.9. (Or perhaps restrain S02 to 0.9; then you're really doing something a bit more like the notion of a Bayesian prior.)
>> 
>> On the other hand, if the S02 is, say, 1.50 +/- 0.07, then the fit really doesn’t like the idea of an S02 in the typical range. An S02 that high, with that small an uncertainty, suggests to me that something is wrong—although it could be as simple as a normalization issue during data reduction. In that case, I’d be more skeptical of just setting S02 to 0.90 and going with that result; the fit is trying to tell you something, and it’s important to track down what that something is.
>> 
>> Of course, once in a while, a fit will find a local minimum, while there’s another good local minimum around a more realistic value. That would be reflected by a fit that gave similarly good quantitative measures of fit quality (e.g. R-factors) when S02 is fit (and yields 1.50 +/- 0.07) as when its forced to 0.90. That’s somewhat unusual, however, particularly with a global parameter like S02.
>> 
>> A good way to defend setting S02 to 0.90 is to use the Hamilton test to see if floating S02 yields a statistically significant improvement over forcing it to 0.90. If not, using your prior best estimate for S02 is reasonable.
>> 
>> If you did that, though, I’d think that it would be good to mention what happened in any eventual publication of presentation; it might provide an important clue to someone who follows up with this or a similar system. It would also be good to increase your reported uncertainty for site occupancy (and indicate in the text what you’ve done). I now see that your site occupancies are 0.53 +/- 0.04 for the floated S02, and 0.72 +/-0.06 for the S02 = 0.90. That’s not so bad, really. It means that you’re pretty confident that the site occupancy is 0.64 +/- 0.15, which isn’t an absurdly large uncertainty as these things go.
>> 
>> To be concrete, if all the Hamilton test does not show statistically significant improvement by floating S02, then I might write something like this in any eventual paper: “The site occupancy was highly correlated with S02 in our fits, making it difficult to determine the site occupancy with high precision. If S02 is constrained to 0.90, a plausible value for element [X] [ref], then the site occupancy is 0.53 +/- 0.04. If constrained to 1.0, the site occupancy is [whatever it comes out to be] To reflect the increased uncertainty associated with the unknown value for S02, we are adopting a value of 0.53 +/- [enough uncertainty to cover the results found for S02 = 1.0].
>> 
>> Of course, if you do that, I’d also suggest tracking down as many other possibilities for why your fit is showing high values of S02 as you can; e.g., double-check your normalization during data reduction.
>> 
>> If, on the other hand, the Hamilton test does show the floated S02 is yielding a statistically significant improvement, I think you have a bigger issue. Looking at, e.g., whether you may have constrained coordination numbers incorrectly becomes more critical.
>> 
>> —Scott Calvin
>> Sarah Lawrence College
>> 
>> 
>> 
>> On Mar 20, 2015, at 12:48 PM, huyanyun at physics.utoronto.ca<mailto:huyanyun at physics.utoronto.ca> wrote:
>> 
>> Hi Scott,
>> 
>> Thank you. Our group has one copy of your book, I'll read it again
>> after my colleague return it to shelf. I still want to continue our
>> discussion here:
>> 
>> If we treat S02 as an empirically observed parameter, can I just set
>> S02=0.9 or 1.45 and let other parameters to explain the k- and R-
>> dependence? Because S02 is not a simplistic parameter which may
>> include both theory and experimental effects, I feel that S02 is not
>> necessarily to be smaller than 1, although I admit S02 smaller than 1
>> is more defensible as it represents some limitations both in theory
>> model and experiment, but I have a series of similar sample and all
>> their S02 will be automatically be fitted to 1.45~1.55, not smaller
>> than 1. Could this indicate something?
>> 
>> I actually found in my system, when I set S02=0.9 (instead of letting
>> it fit to 1.45), other parameter will definitely change but the
>> fitting is not terrible, it is still a close fit but important site
>> occupancy percentage P% changed a lot.  So how should I compare/select
>> from the two fits, one with S02=0.9 and one with S02=1.45 with two
>> scenarios showing different results?
>> 
>> Best,
>> Yanyun
>> Quoting Scott Calvin <scalvin at sarahlawrence.edu<mailto:scalvin at sarahlawrence.edu>>:
>> 
>> Hi Yanyun,
>> 
>> I am hesitant to promote a commercial project from which I directly
>> profit on this list, but it seems to me you are asking a bigger set
>> of questions than can comfortably and sufficiently be answered in
>> this format, and they are questions which have been answered in
>> detail elsewhere.
>> 
>> In my book XAFS for Everyone, I have four pages devoted solely to
>> S02, along with related information elsewhere in the book.
>> 
>> Since you have a University of Toronto address, I am guessing you
>> have access to their library. If you don't wish to purchase the
>> book, you can request it via interlibrary loan, at no cost to you or
>> your institution.
>> 
>> In the mean time, a quote from the book that may be useful in
>> thinking about S02:
>> 
>> "Alternatively, one can treat So2 as a phenomenological parameter
>> that accounts for any amplitude suppression independent of k and R,
>> regardless of physical cause (Krappe and
>> Rossner 2004). Under this view, So2 does not have any particular
>> physical meaning, and the k or R dependence of intrinsic losses can
>> be assigned to other parameters."
>> 
>> That's the way I usually think about it--as not having a single
>> physical meaning, but rather as being an empirically observed
>> correction factor relative to simplistic theories which is
>> indicative both of experimental effects and limitations in the
>> theoretical model.
>> 
>> Hope that helps...
>> 
>> --Scott Calvin
>> Sarah Lawrence College
>> 
>> On Mar 19, 2015, at 6:32 PM, huyanyun at physics.utoronto.ca<mailto:huyanyun at physics.utoronto.ca> wrote:
>> 
>> Hi all,
>> 
>> I know this question has been asked for many times. S02 is expected to
>> be around, but smaller than 1,  a fact that has been explained, such
>> as in the following previous emails, in our mailing list.
>> 
>> http://www.mail-archive.com/ifeffit%40millenia.cars.aps.anl.gov/msg02237.html
>> http://millenia.cars.aps.anl.gov/pipermail/ifeffit/2003-February/000230.html
>> 
>> However, I am continually get S02 value larger than 1 for a series of
>> similar samples when I fit data in Artemis. I think my fit is very
>> good, because my suspected model(based on other technique) could be
>> verified in XAFS analysis (i.e., defensible in physics), the
>> statistics is good ( R=0.01, reduced chi-square=31.4, fit-range:1.5~6
>> Angstrom, k-range: 3~14 angstrom-1) and all the parameters such as the
>> bond length, sigma2 are physically reasonable. The only thing makes me
>> uncomfortable is that parameter S02 keeps between 1.45 to 1.55 during
>> the fitting.
>> 
>> In my system, the absorber atom occupies two crystallographic sites.
>> So I built a model with paths generated from two FEFF calculations.
>> For paths generated from the 1st and 2nd FEFF calculation, the
>> amplitude parameters are set to be S02*P% and S02*(1-P%) respectively,
>> where P% is the first site occupancy percentage. Both S02 and P are
>> free parameters during the fit, and P is an important conclusion I
>> want to extract from XAFS fitting.
>> 
>> However, the fit result gives me S02=1.45 ~ 1.55 and P=0.51 ~ 0.56 all
>> the time (i.e., for each path the 'total amplitude' S02*P% or
>> S02*(1-P%) are about 0.7~0.8, smaller than 1). It looks to me that I
>> got a 'perfect' fit but I am not sure if S02 larger than one is
>> defensible. So I have to ask:
>> 
>> 1) Is my current fit with S02 larger than one reasonable? If not, what
>> could be suggested to get around it?
>> 
>> 2) What's the meaning of S02? It is interpreted in physics that it is
>> a reduced electron excitation parameter, but is it possible that S02
>> will be affected by any experimental condition?
>> 
>> 3) Can anyone share whether you had the multiple site system that gets
>> S02 larger than one?
>> 
>> Looking forward to your help.
>> 
>> Best,
>> Yanyun
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> 
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