An Orientation Selective Neural Network for Pattern Identification in Particle Detectors

Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)

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Halina Abramowicz, David Horn, Ury Naftaly, Carmit Sahar-Pikielny


We present an algorithm for identifying linear patterns on a two(cid:173) dimensional lattice based on the concept of an orientation selective cell, a concept borrowed from neurobiology of vision. Construct(cid:173) ing a multi-layered neural network with fixed architecture which implements orientation selectivity, we define output elements cor(cid:173) responding to different orientations, which allow us to make a se(cid:173) lection decision. The algorithm takes into account the granularity of the lattice as well as the presence of noise and inefficiencies. The method is applied to a sample of data collected with the ZEUS detector at HERA in order to identify cosmic muons that leave a linear pattern of signals in the segmented calorimeter. A two dimensional representation of the relevant part of the detector is used. The algorithm performs very well. Given its architecture, this system becomes a good candidate for fast pattern recognition in parallel processing devices.