Vertex finding by sparse model-based clustering

Rudolf Frühwirth, Korbinian Eckstein, Sylvia Frühwirth-Schnatter

Publication: Scientific journalJournal articlepeer-review

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The application of sparse model-based clustering to the problem of primary vertex finding is discussed. The observed z-positions of the charged primary tracks in a bunch crossing are modeled by a Gaussian mixture. The mixture parameters are estimated via Markov Chain Monte Carlo (MCMC). Sparsity is achieved by an appropriate prior on the mixture weights. The results are shown and compared to clustering by the expectation-maximization (EM) algorithm.
Original languageEnglish
Pages (from-to)012055
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 2016

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