An orientation selective neural network for pattern identification in particle detectors

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We present an algorithm for identifying linear patterns on a two-dimensional lattice based on the concept of an orientation selective cell, a concept borrowed from neurobiology of vision. Constructing a multi-layered neural network with fixed architecture which implements orientation selectivity, we define output elements corresponding to different orientations, which allow us to make a selection 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.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 9 - Proceedings of the 1996 Conference, NIPS 1996
PublisherNeural information processing systems foundation
Pages925-931
Number of pages7
ISBN (Print)0262100657, 9780262100656
StatePublished - 1997
Event10th Annual Conference on Neural Information Processing Systems, NIPS 1996 - Denver, CO, United States
Duration: 2 Dec 19965 Dec 1996

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Conference

Conference10th Annual Conference on Neural Information Processing Systems, NIPS 1996
Country/TerritoryUnited States
CityDenver, CO
Period2/12/965/12/96

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