Stratistical Learning for Parton Identification
The application of methods of statistical learning to the identification of the partons from which hadronic jets originate is investigated using simulated jets in the CDF detector with the ultimate objective of applying them at the trigger level. Using only jet-related properties, it appears to be raltively easy to distinguish between jets originating from gluons and those originating from quarks in an energy-independent manner. Distinguishing between quark flavours is more difficult and will require inclusion of other variables.
KeywordsHEP Parton Identification Multi-Layer Perceptron
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- Badgett, W. et al., Neural networks at the Tevatron, AIHENP2, Agelonde France 1992, Ed. D. Perret Gallix, World Scientific 1992.Google Scholar
- Bianchin, S. et al., in Classification of jets from pp-bar collisions at Tevatron energies, AIHENP2, Agelonde France 1992, Ed. D. Perret Gallix, World Scientific 1992.Google Scholar
- Bianchin, S. et al, Int. J. of Neural Systems, Vol. 3(Supp 1992).Google Scholar
- Corcella, G. et al HERWIG6: An Event Generator for Hadron Emission Reactions With Interfering Gluons, hep-ph/0011363, Cavendish-HEP-99/03, CERN-TH/2000-284.Google Scholar
- Cortes, C. and Vapnik, V. Support Vector Networks in Machine Learning, 20, 1995, pp. 1; K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An introduction to kernel-based learning algorithms, IEEE Neural Networks, 12(2):181–201.Google Scholar
- Sjostrand, T. Lonnblad, L. Mrenna, S. PYTHIA 6.2: Physics and Manual, LU-TP-01-21, Aug 2001, hep-ph/0108264.Google Scholar