Description
Chairs:
2:00-3:40: Gero FLUCKE
4:05-6:00: Pedro TEIXEIRA-DIAS
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Eckhard von Toerne (University of Bonn)06/09/2011, 14:00Track 2 : Data Analysis - Algorithms and ToolsParallel talkThe toolkit for multivariate analysis, TMVA, provides a large set of advanced multivariate analysis techniques for signal/background classification and regression problems. These techniques are embedded in a framework capable of handling input data preprocessing and the evaluation of the results, thus providing a simple and convenient tool for multivariate techniques. The analysis techniques...Go to contribution page
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Prof. Dugan O'Neil (Simon Fraser University (SFU))06/09/2011, 14:25Track 2 : Data Analysis - Algorithms and ToolsParallel talkTau leptons will play an important role in the physics program at the LHC. They will be used in electroweak measurements and in detector related studies like the determination of the missing transverse energy scale, but also in searches...Go to contribution page
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Daniel Zander (Karlsruhe Institute of Technology)06/09/2011, 14:50Track 2 : Data Analysis - Algorithms and ToolsParallel talkFull Reconstruction is an important analysis technique utilized at B factories where B mesons are produced in e+e- -> Y(4S) -> BBbar processes. By reconstructing one of the two B mesons in an event fully in a hadronic final state, the properties of the other B meson are determined using momentum conservation. Therefore, it allows to measure or perform searches for rare B meson decays involving...Go to contribution page
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Daniel Martschei (Inst. für Experimentelle Kernphys.-Universitaet Karlsruhe-KIT)06/09/2011, 15:15Track 2 : Data Analysis - Algorithms and ToolsParallel talkTitle: Advanced event reweighting for MVA training. Multivariate discrimination techniques, such as Neural Networks, are key ingredients to modern data analysis and play an important role in high energy physics. They are usually trained on simulated Monte Carlo (MC) samples to discriminate signal from background and are then applied to data. This has in general some side effects which we...Go to contribution page
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Dr Federico Colecchia (University College London)06/09/2011, 16:10Track 2 : Data Analysis - Algorithms and ToolsParallel talkBackground properties in experimental particle physics are typically estimated from large collections of events. This usually provides precise knowledge of average background distributions, but inevitably hides fluctuations. To overcome this limitation, an approach based on statistical mixture model decomposition is presented. Events are treated as heterogeneous populations comprising...Go to contribution page
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Mr Mikael Kuusela (Helsinki Institute of Physics (HIP))06/09/2011, 16:35Track 2 : Data Analysis - Algorithms and ToolsParallel talkMost classification algorithms used in high energy physics fall under the category of supervised machine learning. Such methods require a training set containing both signal and background events and are prone to classification errors should this training data be systematically inaccurate for example due to the assumed MC model. To complement such model-dependent searches, we propose an...Go to contribution page
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Dr Silvia Tentindo (Department of Physics-Florida State University)06/09/2011, 17:00Track 2 : Data Analysis - Algorithms and ToolsParallel talkNeural networks (NN) are universal approximators. Therefore, in principle, it should be possible to use them to model any reasonably smooth probability density such as the probability density of fake missing transverse energy (MET). The modeling of fake MET is an important experimental issue in events such as $Z \rightarrow l^+ l^-$+jets, which is an important background in high-mass Higgs...Go to contribution page