A new jet algorithm based on the k-means clustering for the reconstruction of heavy states from jets

Abstract

A jet algorithm based on the k-means clustering procedure is proposed which can be used for the invariant-mass reconstruction of heavy states decaying to hadronic jets. The proposed algorithm was tested by reconstructing E+ E- to ttbar to 6 jets and E+ E- to W+W- to 4 jets processes at \sqrt{s}=500 GeV using a Monte Carlo simulation. It was shown that the algorithm has a reconstruction efficiency similar to traditional jet-finding algorithms, and leads to 25% and 40% reduction of reconstruction width for top quarks and W bosons, respectively, compared to the kT (Durham) algorithm. In addition, it is expected that the peak positions measured with the new algorithm have smaller systematical uncertainty.

11 pages, 3 eps figures (Eur. Phys. J. C, in press)

References in corpus (3)

• New and Old Jet Clustering Algorithms for Electron-Positron Events
• KtJet: A C++ implementation of the Kt clustering algorithm
• Selection and reconstruction of the top quarks in the all-hadronic decays at a Linear Collider

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