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This is achieved using the clustering method DBSCAN \cite{Sander_1998}. The works in the parameter space and finds clusters of arbitrary shape based on the density of events. This is preferable to standard clustering methods which often yield radially symmetric clusters (k-means, etc).   Clustering is performed twice. First pixel events are clustered accoring to their shape i.e., clustering is done on matrix \(E_s\). \(E^s\).  This step distributes pixels into several groups based on solely their shape (e.g., groups of elementary events composing spark and wave events). In the second clustering step, the \(E^p\) matrix is cluster for each shape group and physically nearby clusters of similar events are obtained. With this two-step approach, release events of various types consisting on elementary events from multiple pixels are obtained.