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Rustem Ospanov

Researcher at University of Science and Technology of China

Publications -  951
Citations -  77322

Rustem Ospanov is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 126, co-authored 834 publications receiving 70155 citations. Previous affiliations of Rustem Ospanov include Universidade Nova de Lisboa & West University of Timișoara.

Papers
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Journal ArticleDOI

Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC

Georges Aad, +2967 more
- 17 Sep 2012 - 
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.
Journal ArticleDOI

Combined Measurement of the Higgs Boson Mass in pp Collisions at √s=7 and 8 TeV with the ATLAS and CMS Experiments

Georges Aad, +5120 more
- 14 May 2015 - 
TL;DR: A measurement of the Higgs boson mass is presented based on the combined data samples of the ATLAS and CMS experiments at the CERN LHC in the H→γγ and H→ZZ→4ℓ decay channels.
Journal ArticleDOI

The ATLAS Simulation Infrastructure

Georges Aad, +2585 more
- 25 Sep 2010 - 
TL;DR: The simulation software for the ATLAS Experiment at the Large Hadron Collider is being used for large-scale production of events on the LHC Computing Grid, including supporting the detector description, interfacing the event generation, and combining the GEANT4 simulation of the response of the individual detectors.
Posted Content

TMVA - Toolkit for Multivariate Data Analysis

Andreas Hoecker, +26 more
- 04 Mar 2007 - 
TL;DR: The TMVA toolkit as discussed by the authors is a toolkit for multivariate regression of a real-valued target vector and has been extended to multivariate classification of a target vector with the same user interfaces as classification.

TMVA - Toolkit for Multivariate Data Analysis

Andreas Hocker, +14 more
TL;DR: TMVA as mentioned in this paper is a toolkit that hosts a large variety of multivariate classification algorithms, ranging from rectangular cut optimization using a genetic algorithm and from one-dimensional likelihood estimators, over linear and nonlinear discriminants and neural networks, to sophisticated more recent classifiers such as a support vector machine, boosted decision trees and rule ensemble fitting.