[Ifeffit] Normalization of LCF coefficients when fitting experimental spectra with theoretical spectra
Mangold, Stefan (IPS)
stefan.mangold at kit.edu
Thu Mar 11 01:01:23 CST 2021
Linear combination fits are a nice tool, but one should handle them with great care
- the background subtraction process can introduce significant errors. You have to fine tune the parameters by hand if you are using Athena to get reasonable results
- sometimes PCA (principle component analysis) helps to find a reasonable number of components
- Athena only shows the fitting errors: please keep in mind, that the error of the complete measurement and data evaluation process is much larger. It makes sense to test the process with known mixtures.
- the variation of the of the sum of all components between 98 and 105 % (even with choosing -> sum coefficients to 1) is a problem somewhere inside of Athena. I think only Bruce Ravel can help you there.
> Am 08.03.2021 um 19:27 schrieb Patricia Poths <patriciapoths at chem.ucla.edu>:
> I am a theoretical chemistry PhD student working on fitting experimental spectra with computed spectra in order to get a better understanding of the composition. In the LCF process with athena, I have found that when I allow "sum coefficients to 1", I get an unphysical negative coefficient of the last standard- after reading through the mailing list I understood why, and so no longer use that. The sum of my coefficients during the fitting now is close to 1- generally within the range of 0.95-1.1 at the absolute extremes, but more often around ~0.98- ~1.05.
> In order to compare these coefficients, I renormalize them to 1, so they can represent the fractions of each component present. However, to test this I took the new normalized coefficients and summed up the standards with their respective weights to create this normalized "fit", and found that it is worse than the initial one. Is this something I should be concerned about when reporting the qualitative trends in how the composition changes? And if so, is there a better way to do the fits in a more normalized way?
> Many thanks,
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