deletions | additions       diff --git a/res_fitfunction.md b/res_fitfunction.md  index fc0c8d9..fcc3efd 100644  --- a/res_fitfunction.md  +++ b/res_fitfunction.md 
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\[  f(A, d, \tau_{d}, \tau_{r}, \mu, t, \sigma) = g \ast G  \]  For notational purposes we shall represent the fit function parameters by . \(\mathbf{p}=  \left[  \begin{array}{c c c c c}  A&d&\tau{d}&\tau_{r}&\mu  \end{array}  \right]  \).  The smoothing parameter \(\sigma\) will be fixed for all pixels. Therefore, for the *i*-th pixel the *k*-th event is represented by \(f(\mathbf{r}_{i,k},t)\). 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.  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.