Abstract
Elementary particle physics includes a number of feature recognition problems for which artificial neural networks can be used. We used a feed-forward neural network to seperate particle jets originating from b-quarks from other jets. Some aspects such as pruning and overfitting have been studied. Furthermore, the influence of modifications in architecture and input space have been examined. In addtition we discuss how self-organizing networks can be applied to high energy physics problems.
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References
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L. Lönnblad, C. Peterson, H. Pi, T. Rögnvaldsson Self-organizing Networks for Extracting Jet Features, Preprint Lund University, LU TP 91-4 (1991)
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© 1992 Springer-Verlag Berlin Heidelberg
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Becks, K.H., Dahm, J., Seidel, F. (1992). Analysing particle jets with artificial neural networks. In: Belli, F., Radermacher, F.J. (eds) Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. IEA/AIE 1992. Lecture Notes in Computer Science, vol 604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0024961
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DOI: https://doi.org/10.1007/BFb0024961
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