Institution

# University of Wuppertal

Education•Wuppertal, Nordrhein-Westfalen, Germany•

About: University of Wuppertal is a education organization based out in Wuppertal, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Large Hadron Collider & Quantum chromodynamics. The organization has 5378 authors who have published 12051 publications receiving 354621 citations.

##### Papers published on a yearly basis

##### Papers

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TL;DR: In this article, a search for the Standard Model Higgs boson in proton-proton collisions with the ATLAS detector at the LHC is presented, which has a significance of 5.9 standard deviations, corresponding to a background fluctuation probability of 1.7×10−9.

Abstract: A search for the Standard Model Higgs boson in proton–proton collisions with the ATLAS detector at the LHC is presented. The datasets used correspond to integrated luminosities of approximately 4.8 fb−1 collected at View the MathML source in 2011 and 5.8 fb−1 at View the MathML source in 2012. Individual searches in the channels H→ZZ(⁎)→4l, H→γγ and H→WW(⁎)→eνμν in the 8 TeV data are combined with previously published results of searches for H→ZZ(⁎), WW(⁎), View the MathML source and τ+τ− in the 7 TeV data and results from improved analyses of the H→ZZ(⁎)→4l and H→γγ channels in the 7 TeV data. Clear evidence for the production of a neutral boson with a measured mass of View the MathML source is presented. This observation, which has a significance of 5.9 standard deviations, corresponding to a background fluctuation probability of 1.7×10−9, is compatible with the production and decay of the Standard Model Higgs boson.

9,282 citations

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TL;DR: In this paper, the correlation exponent v is introduced as a characteristic measure of strange attractors which allows one to distinguish between deterministic chaos and random noise, and algorithms for extracting v from the time series of a single variable are proposed.

Abstract: We study the correlation exponent v introduced recently as a characteristic measure of strange attractors which allows one to distinguish between deterministic chaos and random noise. The exponent v is closely related to the fractal dimension and the information dimension, but its computation is considerably easier. Its usefulness in characterizing experimental data which stem from very high dimensional systems is stressed. Algorithms for extracting v from the time series of a single variable are proposed. The relations between the various measures of strange attractors and between them and the Lyapunov exponents are discussed. It is shown that the conjecture of Kaplan and Yorke for the dimension gives an upper bound for v. Various examples of finite and infinite dimensional systems are treated, both numerically and analytically.

5,239 citations

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01 Jan 1997TL;DR: Using nonlinear methods when determinism is weak, as well as selected nonlinear phenomena, is suggested to be a viable alternative to linear methods.

Abstract: Part I. Basic Concepts: 1. Introduction: why nonlinear methods? 2. Linear tools and general considerations 3. Phase space methods 4. Determinism and predictability 5. Instability: Lyapunov exponents 6. Self-similarity: dimensions 7. Using nonlinear methods when determinism is weak 8. Selected nonlinear phenomena Part II. Advanced Topics: 9. Advanced embedding methods 10. Chaotic data and noise 11. More about invariant quantities 12. Modeling and forecasting 13. Chaos control 14. Other selected topics Appendix 1. Efficient neighbour searching Appendix 2. Program listings Appendix 3. Description of the experimental data sets.

4,019 citations

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23 Feb 2020

TL;DR: The ATLAS detector as installed in its experimental cavern at point 1 at CERN is described in this paper, where a brief overview of the expected performance of the detector when the Large Hadron Collider begins operation is also presented.

Abstract: The ATLAS detector as installed in its experimental cavern at point 1 at CERN is described in this paper. A brief overview of the expected performance of the detector when the Large Hadron Collider begins operation is also presented.

3,111 citations

01 Nov 2003

TL;DR: In this article, the authors discuss the use of non-linear methods when determinism is weak and apply them to the problem of neighbor search in the presence of chaotic data and noise.

Abstract: Part I. Basic Concepts: 1. Introduction: why nonlinear methods? 2. Linear tools and general considerations 3. Phase space methods 4. Determinism and predictability 5. Instability: Lyapunov exponents 6. Self-similarity: dimensions 7. Using nonlinear methods when determinism is weak 8. Selected nonlinear phenomena Part II. Advanced Topics: 9. Advanced embedding methods 10. Chaotic data and noise 11. More about invariant quantities 12. Modeling and forecasting 13. Chaos control 14. Other selected topics Appendix 1. Efficient neighbour searching Appendix 2. Program listings Appendix 3. Description of the experimental data sets.

2,158 citations

##### Authors

Showing all 5542 results

Name | H-index | Papers | Citations |
---|---|---|---|

Wolfgang Wagner | 156 | 2342 | 123391 |

Teresa Lenz | 150 | 1718 | 114725 |

Hermann Kolanoski | 145 | 1279 | 96152 |

Alexander Milov | 142 | 1143 | 93374 |

Peter Wagner | 137 | 1512 | 99949 |

Frank Ellinghaus | 135 | 956 | 82266 |

Josef Strauss | 131 | 1001 | 83457 |

John Hobbs | 131 | 1341 | 88880 |

Markus Elsing | 131 | 1111 | 82757 |

P. Mättig | 131 | 1366 | 94022 |

James Pinfold | 130 | 1327 | 86989 |

Igor Volobouev | 129 | 1417 | 93220 |

Gerhard Brandt | 129 | 949 | 75348 |

Torsten Harenberg | 128 | 866 | 75731 |

Tobias Flick | 128 | 872 | 75059 |