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H. Schiller

Bio: H. Schiller is an academic researcher. The author has contributed to research in topics: Multidimensional analysis. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

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Journal ArticleDOI
TL;DR: In this article, an approach based on a nonparametric ωnk goodness-of-fit criterion was proposed to solve the problem of electron/pion identification in the CBM experiment.
Abstract: The problem of electron/pion identification in the CBM experiment based on the measurements of energy losses and transition radiation in the TRD detector is discussed. Earlier we analyzed a possibility to solve such a problem using an artificial neural network (ANN) [1]. Here we consider an approach based on a nonparametric ωnk goodness-of-fit criterion, and comparison with the ANN method is also performed. We show that both methods provide a comparable level of pion suppression and electron identification, the ωnk test is more simple for practical applications, the ANN method provides the needed level of pions suppression only if “clever” variables are used. We demonstrate that application of the ωnk-criterion to the J/Ψ reconstruction provides a high level of pion background suppression and significantly improves a signal-to-background ratio.

3 citations

01 Jan 2008
TL;DR: In this paper, an approach based on a nonparametric ω n goodness-of-ˇt criterion was proposed to solve the problem of electron/pion identiˇcation in the CBM experiment.
Abstract: The problem of electron/pion identiˇcation in the CBM experiment based on the measurements of energy losses and transition radiation in the TRD detector is discussed. Earlier we analyzed a possibility to solve such a problem using an artiˇcial neural network (ANN) [1]. Here we consider an approach based on a nonparametric ω n goodness-of-ˇt criterion, comparison with the ANN method is also performed. We show that both methods provide a comparable level of pion suppression and electron identiˇcation, the ω n test is more simple for practical applications, the ANN method provides the needed level of pions suppression only if ®clever variables are used. We demonstrate that application of the ω n-criterion to the J/ψ reconstruction provides a high level of pion background suppression and signiˇcantly improves a signal-to-background ratio.

3 citations

Book ChapterDOI
13 Dec 2000
TL;DR: The multivariate data analysis based on Ω n k -criteria and artificial neural networks and the use of cellular automata in pattern recognition and in modeling of complex dynamical systems are considered.
Abstract: This review is devoted to the computational methods and tools for modeling and analysis of various complex processes in physics, medicine, social dynamics and nature. We consider: 1) the multivariate data analysis based on Ω n k -criteria and artificial neural networks (ANN), 2) the applications of neural networks for the function approximation and for the reconstruction and prediction of chaotic time series, and 3) the use of cellular automata (CA) in pattern recognition and in modeling of complex dynamical systems.
Journal ArticleDOI
TL;DR: Program and data structures in the off-line analysis of large high energy physics experiments are discussed and concepts for general data analysis software and new analysis techniques are discussed with respect to the special conditions of HEP experiments.