Self-organizing networks for extracting jet features

• 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.

Ämnesord

NATURVETENSKAP  -- Fysik -- Subatomär fysik (hsv//swe)

NATURAL SCIENCES  -- Physical Sciences -- Subatomic Physics (hsv//eng)

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