Abstract
The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the use of multi-layer perceptrons for event classification and function approximation. This network type is best suited for a hardware implementation and special VLSI chips are available which are used in fast trigger processors. Also discussed are self-organizing networks for the recognition of features in large data samples. Neural net algorithms like the Hopfield model have been applied for pattern recognition in tracking detectors.
Cite
CITATION STYLE
Kolanoski, H. (1996). Application of artificial neural networks in particle physics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1112 LNCS, pp. 1–14). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_1
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