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Münster 2017 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 23: Experimentelle Techniken der Astroteilchenphysik 2

Montag, 27. März 2017, 16:45–19:00, S 055

16:45 T 23.1 Deep Learning für Neutrinoteleskope — •Stefan Geißelsöder für die ANTARES-KM3NeT-Erlangen Kollaboration
17:00 T 23.2 Deep Learning für KM3NeT — •Christoph Biernoth für die ANTARES-KM3NeT-Erlangen Kollaboration
17:15 T 23.3 Deep Learning in Physics exemplified by the reconstruction of muon-neutrino events in IceCube — •Mirco Hünnefeld for the IceCube collaboration
17:30 T 23.4 Mining for Spectra - The Dortmund Spectrum Estimation AlgorithmTim Ruhe and •Thorben Menne
17:45 T 23.5 Online Classification of IceCube Events using Neural Networks — •Joshua Luckey for the IceCube collaboration
18:00 T 23.6 Improvement of energy reconstruction by using machine learning algorithms in MAGIC — •Kazuma Ishio, Galina Maneva, Abelardo Moralejo, David Paneque, Julian Sitarek, and Petar Temnikov for the MAGIC collaboration
18:15 T 23.7 Neural Networks for Energy Reconstruction in the IceCube Neutrino Observatory — •Martin Brenzke, Jan Auffenberg, Christian Haack, René Reimann, and Christopher Wiebusch for the IceCube collaboration
18:30 T 23.8 Event Identification for KM3NeT/ARCA — •Thomas Heid for the ANTARES-KM3NeT-Erlangen collaboration
18:45 T 23.9 Dealing with Data/Simulation Mismatches in Machine Learning based Analyses — •Mathis Börner, Jens Buß, and Thorben Menne for the IceCube collaboration
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