20130225 Nonlinear Scan Optimisation

Nonlinear scans often produce a fit uncertainty for sigma of about 0.1um. This is ok, however, better is always better and more importantly, fitting the same data repeatedly often yields different results that can be different by greater than the uncertainty. Whilst not ideal, this can be understood as being a four parameter fit, it is often possible to minimise successfully with one parameter different from a previous fitting by adjusting the others. This is usually shown with data from different scans as each scan is unique and the route taken to minimum often is also. Therefore, if we can't improve this, we should improve the uncertainty on individual points and secondly (more for time purposes) the number of points and their location.

More samples corresponds to greater statistics but also a greater overall length of scan (linear in number of samples), however, the benefit from the statistics reduces as it's dependent on 1/sqrt(nsamples). At some point, a significantly

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Topic revision: r1 - 25 Feb 2013 - LaurieNevay

 
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