deletions | additions       diff --git a/res_fitfunction.md b/res_fitfunction.md  index 0b6001d..4d9631d 100644  --- a/res_fitfunction.md  +++ b/res_fitfunction.md 
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For the optimizer to obtain good performance when fitting the function used for fitting should be continously differentiable. With this in mind, the transient function is convolved with a gaussian \(G(\sigma)\) to yield the actual fitting function:  \[  f(A, d, \tau_{d}, \tau_{r}, \mu, t, \sigma) = g \ast G  \]  For notational purposes we shall represent the fit function parameters by \(\mathbf{p}= 
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The entire raw signal for the *i*-th pixel can be represented as:  \[  \label{eq_ithpix} F_i(t) =  b(\mathbf{q}_i, t)+ \sum_{k=0}^{m} f(\mathbf{r}_{i,k},t) + W + R \]  , 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, \(W\) represents noise and \(R\) is the remaining residual not captured in the baseline nor events. Ideally, \(R=0\), but achieving this is 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.