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  • Analysis of the experimental data has one of the most important roles in High Energy Physics. Commonly used multivariate techniques as Boosted Decision Trees or Bayesian Neural Networks are based on learning algorithms using Monte Carlo generated samples. We implemented a new Model Based Clustering (MBC) method including the use of Bayesian statistics and modified iterative EM algorithm for weighted data that have never been applied in this area. This greatly promising method was developed especially for the data collected from the D0 experiment, which was one of two large particle physics experiments at the pp- Tevatron collider at Fermilab. We optimized and tested proposed method in the single top search using a data sample of 9.7 fb-1 of integrated luminosity, which corresponds to the entire Run II D0 dataset.
  • Analysis of the experimental data has one of the most important roles in High Energy Physics. Commonly used multivariate techniques as Boosted Decision Trees or Bayesian Neural Networks are based on learning algorithms using Monte Carlo generated samples. We implemented a new Model Based Clustering (MBC) method including the use of Bayesian statistics and modified iterative EM algorithm for weighted data that have never been applied in this area. This greatly promising method was developed especially for the data collected from the D0 experiment, which was one of two large particle physics experiments at the pp- Tevatron collider at Fermilab. We optimized and tested proposed method in the single top search using a data sample of 9.7 fb-1 of integrated luminosity, which corresponds to the entire Run II D0 dataset. (en)
Title
  • Model based clustering method as a new multivariate technique in high energy physics
  • Model based clustering method as a new multivariate technique in high energy physics (en)
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  • Model based clustering method as a new multivariate technique in high energy physics
  • Model based clustering method as a new multivariate technique in high energy physics (en)
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  • RIV/68407700:21340/13:00210459!RIV14-MSM-21340___
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  • P(LG12020), S
http://linked.open...vai/riv/dodaniDat
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  • 88715
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  • RIV/68407700:21340/13:00210459
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  • Model Based Clustering; High Energy Physics; Data analysis; Signal and Background Separation; Single Top Quark; ROC (en)
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  • [08054DD6B27A]
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  • Nebřich
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  • Praha
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  • SPMS 2013 Stochastic and Physical Monitoring Systems Proceedings
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  • Franc, Jiří
  • Štěpánek, Michal
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number of pages
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  • České vysoké učení technické v Praze
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  • 978-80-01-05383-6
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  • 21340
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