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Methods for Multidimensional Event Classification: A Case Study using Images from a Cherenkov Gamma-Ray Telescope
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SYSNO ASEP 0103275 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Methods for Multidimensional Event Classification: A Case Study using Images from a Cherenkov Gamma-Ray Telescope Title Metody vícerozměrné klasifikace událostí: Aplikace na signál z teleskopu pro detekci Čerenkovova gamma záření Author(s) Bock, R.K. (DE)
Chilingarian, A. (RU)
Gaug, M. (ES)
Hakl, František (UIVT-O) SAI, RID, ORCID
Hengstebeck, T. (DE)
Jiřina, Marcel (UIVT-O) SAI, RID
Klaschka, Jan (UIVT-O) RID, SAI, ORCID
Kotrč, Emil (UIVT-O)
Savický, Petr (UIVT-O) SAI, RID, ORCID
Towers, S. (US)
Vaicilius, A. (US)
Wittek, W. (DE)Source Title Nuclear Instruments & Methods in Physics Research Section A. - : Elsevier - ISSN 0168-9002
Roč. 516, - (2004), s. 511-528Number of pages 18 s. Language eng - English Country NL - Netherlands Keywords classification ; discrimination ; multivariate ; neural networks ; kerlen methods ; nearest-neighbour ; regression trees Subject RIV BA - General Mathematics R&D Projects LN00A056 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) GA201/00/1482 GA ČR - Czech Science Foundation (CSF) LN00B096 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) UT WOS 000188083200026 EID SCOPUS 0346846461 DOI https://doi.org/10.1016/j.nima.2003.08.157 Annotation We present results from a case study comparing different multivariate classification methods. The input is a set of Monte Carlo data, generated and approximately triggered and pre-processed for an imaging gamma-ray Cherenkov telescope. Such data belong to two classes, originating either from incident gamma rays or caused by hadronic showers. There is only a weak discrimination between signal (gamma) and background (hadrons), making the data an excellent proving ground for classification techniques. The data and methods are described, and a comparison of the results is made. Several methods give results comparable in quality within small fluctuations, suggesting that they perform at or close to the Bayesian limit of achievable separation. Other methods give clearly inferior or inconclusive results. Some problems that this study can not address are also discussed. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2005
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