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Model based clustering method as a new multivariate technique in high energy physics

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F13%3A00210459" target="_blank" >RIV/68407700:21340/13:00210459 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Model based clustering method as a new multivariate technique in high energy physics

  • Original language description

    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 integratedluminosity, which corresponds to the entire Run II D0 dataset.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    BB - Applied statistics, operational research

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/LG12020" target="_blank" >LG12020: Advanced statistical analysis and non-statistical separation techniques for physical processing detection in data sets sampled by means of elementary particle accelerators.</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2013

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    SPMS 2013 Stochastic and Physical Monitoring Systems Proceedings

  • ISBN

    978-80-01-05383-6

  • ISSN

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    151-156

  • Publisher name

    ČVUT

  • Place of publication

    Praha

  • Event location

    Nebřich

  • Event date

    Jun 24, 2013

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article