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Application of the neural networks in events classification in the measurement of the spin structure of the deuteron

R Sulej1, K Zaremba1, K Kurek2 and E Rondio2

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In this paper, we present the application of a neural network for events classification in a high-energy physics experiment. As a network model we use a multi-layer perceptron with a dynamic topology adjustment algorithm. Our solution covers both adding new hidden neuron units and removing unnecessary units. Neural network results are compared to the standard kinematical cuts techniq1guuuuue and to the well-known k-nearest neighbour classifier.


PACS

07.05.Mh Neural networks, fuzzy logic, artificial intelligence

21.10.Hw Spin, parity, and isobaric spin

21.45.-v Few-body systems

29.20.dk Synchrotrons

Subjects

Accelerators, beams and electromagnetism

Nuclear physics

Instrumentation and measurement

Particle physics and field theory

Dates

Issue 8 (August 2007)

Received 31 October 2006, in final form 10 January 2007

Published 6 July 2007



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