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arXiv · hep-ex/9602006

An Orientation Selective Neural Network and its Application to Cosmic Muon Identification

Halina Abramowicz·David Horn·Ury Naftaly·Carmit Sahar-Pikielny
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Abstract

We propose a novel method for identification of a linear pattern of pixels on a two-dimensional grid. Following principles employed by the visual cortex, we employ orientation selective neurons in a neural network which performs this task. The method is then applied to a sample of data collected with the ZEUS detector at HERA in order to identify cosmic muons which leave a linear pattern of signals in the segmented uranium-scintillator calorimeter. A two dimensional representation of the relevant part of the detector is used. The results compared with a visual scan point to a very satisfactory cosmic muon identification. The algorithm performs well in the presence of noise and pixels with limited efficiency. Given its architecture, this system becomes a good candidate for fast pattern recognition in parallel processing devices.

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Halina Abramowicz, David Horn, Ury Naftaly, Carmit Sahar-Pikielny. 1996-02-15. An Orientation Selective Neural Network and its Application to Cosmic Muon Identification. https://doi.org/10.1016/0168-9002(96)00417-2

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