Selforganizing networks for extracting jet features
Mar, 1991
26 pages
Published in:
- Comput.Phys.Commun. 67 (1991) 193-209
Report number:
- LU-TP-91-4
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Abstract: (Elsevier)
Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model discrimination and separation of b, c and light quarks.- electron positron: annihilation
- annihilation: electron positron
- jet: electroproduction
- electroproduction: jet
- final state: (2jet)
- (2jet): final state
- quark: jet
- quark: hadronization
- quark: flavor
- Z0
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