Description
Chairs:
2:00-3:40: Gero FLUCKE
4:05-6:00: Pedro TEIXEIRA-DIAS
Eckhard von Toerne
(University of Bonn)
06/09/2011, 14:00
Track 2 : Data Analysis - Algorithms and Tools
Parallel talk
The 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...
Prof.
Dugan O'Neil
(Simon Fraser University (SFU))
06/09/2011, 14:25
Track 2 : Data Analysis - Algorithms and Tools
Parallel talk
Tau 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...
Daniel Zander
(Karlsruhe Institute of Technology)
06/09/2011, 14:50
Track 2 : Data Analysis - Algorithms and Tools
Parallel talk
Full 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...
Daniel Martschei
(Inst. für Experimentelle Kernphys.-Universitaet Karlsruhe-KIT)
06/09/2011, 15:15
Track 2 : Data Analysis - Algorithms and Tools
Parallel talk
Title: 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...
Dr
Federico Colecchia
(University College London)
06/09/2011, 16:10
Track 2 : Data Analysis - Algorithms and Tools
Parallel talk
Background 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...
Mr
Mikael Kuusela
(Helsinki Institute of Physics (HIP))
06/09/2011, 16:35
Track 2 : Data Analysis - Algorithms and Tools
Parallel talk
Most 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...
Dr
Silvia Tentindo
(Department of Physics-Florida State University)
06/09/2011, 17:00
Track 2 : Data Analysis - Algorithms and Tools
Parallel talk
Neural 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...
