Hello MRSHub Community,

I am facing some problems understanding and interpreting the Gannet Fit Error from Gannet. I believe that the formula for Fit Error computation found on the website (Data quality metrics) differs from the one found in the paper (https://onlinelibrary.wiley.com/doi/full/10.1002/jmri.24478). I have also attempted to understand the Gannet fit code on GitHub and believe that it uses the formula from the paper, which involves taking the standard deviation of the fitting residual divided by the amplitude of the fitted peak.

I have some questions regarding the reliability of this metric for evaluating spectral quality. For instance, if two signals have the same standard deviation but one of them has a wrongly overestimated fitted peak, it would receive a better quality metric. Does it make sense to consider this scenario?

Also, is it possible to calculate the Cramer-Rao lower bound (CRLB) through Gannet? If not, how could I implement it?

Thank you for the help,

Gabriel

Hi @Gabriel_Dias,

Thank you for pointing out the error of the fit error description on the Gannet website! I’ve been meaning to complete that page. I’ve corrected it. It now matches the formula as shown in the original Gannet paper (except for the factor of `100`

that is applied that is not reported in the paper).

Regarding your second point regarding the two signals scenario: I’m not quite understanding your point as this is not plausible. A wrongly overestimated fitted peak in Gannet would by necessity have large residuals, so the fit error would consequently be higher than the correctly estimated peak. Could you please clarify what you mean?

Gannet does not calculate CRLBs as this error estimation approach does not suit the modeling we use. If we were using basis sets, then it would perhaps make sense.

I hope this helps.

Mark

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Hi @mmikkel,

Regarding my second point, after reading the Gannet documentation more carefully, I realize that it was not plausible.

Thank you very much for all the information; it was really helpful.

Gabriel

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