Selection of W-pair-production in DELPHI with feed-forward neural networks | Zendy

K. H. Becks | Zendy

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Selection of W-pair-production in DELPHI with feed-forward neural networks

Author(s) -

K. H. Becks

Publication year - 2001

Publication title -

aip conference proceedings

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.177

H-Index - 75

eISSN - 1551-7616

pISSN - 0094-243X

DOI - 10.1063/1.1405267

Subject(s) - large hadron collider , selection (genetic algorithm) , delphi , physics , production (economics) , nuclear physics , hadron , particle physics , center of mass (relativistic) , artificial neural network , collider , computer science , artificial intelligence , operating system , mechanics , economics , macroeconomics , energy–momentum relation

Since 1998 feed-forward networks have been applied for the separation of hadronic WW-decays from background processes measured by the DELPHI collaboration at different center-of-mass energies of the Large Electron Positron collider at CERN. Prior to the publication of the 189 GeV results (1) intensive studies of systematic effects and uncertainties were performed. The methods and results will be discussed and compared to standard selection procedures.

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