Divergence separation techniques for high energy physics data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F15%3A00236279" target="_blank" >RIV/68407700:21340/15:00236279 - 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
Divergence separation techniques for high energy physics data
Original language description
Binary decision trees are widely used tool for unsupervised classication of high-dimensional data, for example among particle physicists. We present our proposal of the super-vised binary divergence decision tree with nested separation method based on the generalized linear models. A key insight we provide is the clustering driven only by a few selected physi-cal variables. The proper selection consists of the variables achieving the maximal divergence measure between two different classes. Further we apply our method to Monte Carlo data set simulating measured observations from the particle accelerator at D? experiment in Fermilab. We also introduce the modification of statistical tests applicable to weighted data sets in order to test homogeneity ofthe Monte Carlo simulation and real data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BF - Elementary particle theory and high energy physics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/LG15047" target="_blank" >LG15047: Collaboration on experiments in Fermi National Accelerator Laboratory, USA</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
2015
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 2015 - Stochastic and Physical Monitoring Systems - Proceedings
ISBN
978-80-01-05841-1
ISSN
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e-ISSN
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Number of pages
13
Pages from-to
7-19
Publisher name
ČVUT
Place of publication
Praha
Event location
Drhleny
Event date
Jun 22, 2015
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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