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
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DOI - Digital Object Identifier
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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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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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
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e-ISSN
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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
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