deletions | additions       diff --git a/res_fitting.md b/res_fitting.md  index f376b66..3e5bdf2 100644  --- a/res_fitting.md  +++ b/res_fitting.md 
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\[  F_i(t) = b(\mathbf{q}_i, t) + \sum_{k=0}^{m} f(\mathbf{p}_{i,k},t) + W + R  \]  , where \(b\) is a n-th order polynomial with \(\mathbf{q}_i\) being the polynomial  coefficients for the *i*-th pixel, summation is performed over all *m* events in the pixel, \(W\) consists of the noise and \(R\) is remaining residual not captured in the baseline nor events. Ideally, \(R=0\), but in achieving this we are limited by the accuracy of the event region detection (we cannot fit what we do not detect) and whether or not our fitting function is general enough to be able to approximate various types of events. Because it is not know which part of the signal is the event and which is the baseline the first fit also has to estimate the baseline properties.  Signal in the candidate region is fitted with an extended fit function (Figure \ref{fig:fig2}) that also depends on relaxation baseline \(B\) and baseline offset \(C\). The \(C\) parameter allows for the possibility of an elevated background before the release event.   After paramater optimziation optimization  with the extended fit function the same signal is fitted with a line. For both models the corrected Akaike Information Criterion (AICc \cite{Burnham_2004}) is calculated and the region is accepted only if the AICc for the fit function is less than the AICc for the line. This ensures that the goodness of the fit obtained with the fit function justifies the use of a more complicated model.