Ardo Illaste edited res_fitting2.md  almost 10 years ago

Commit id: a9992e852c2e3d6fe005007db3e239d035bfb900

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=\left[A_i, d_i ,\tau_{d,i},\tau_{r,i},\mu_i,x_i,y_i\right] =  \left[\mathbf{p}^s_{i}, \mathbf{p}^p_{i}\right]  \]  , where \(\mathbf{p}^s=\left[A,d,\tau{d},\tau_{r}\right]\) \(\mathbf{p}^s_i=\left[A_i,d_i,\tau_{d}_i,\tau_{r}_i\right]\)  is the shape parameter vector, \(\mathbf{p}^p=\left[\mu,x,y\right]\) \(\mathbf{p}^p_i=\left[\mu_i,x_i,y_i\right]\)  is the position parameter vector and the whole parameter vector \(\mathbf{r}_i\) is obtained by combining the two. The *k*-th event detected in pixel *i* is represented by vectors   \(\mathbf{p}^s_{i,k}\) and \(\mathbf{p}^p_{i,k}\). These vectors can be stacked to create an event matrix (E) for pixel *i*: