T 23: Experimentelle Techniken der Astroteilchenphysik 2
Monday, March 27, 2017, 16:45–19:00, S 055
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16:45 |
T 23.1 |
Deep Learning für Neutrinoteleskope — •Stefan Geißelsöder für die ANTARES-KM3NeT-Erlangen Kollaboration
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17:00 |
T 23.2 |
Deep Learning für KM3NeT — •Christoph Biernoth für die ANTARES-KM3NeT-Erlangen Kollaboration
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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
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17:30 |
T 23.4 |
Mining for Spectra - The Dortmund Spectrum Estimation Algorithm — Tim Ruhe and •Thorben Menne
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17:45 |
T 23.5 |
Online Classification of IceCube Events using Neural Networks — •Joshua Luckey for the IceCube collaboration
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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
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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
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18:30 |
T 23.8 |
Event Identification for KM3NeT/ARCA — •Thomas Heid for the ANTARES-KM3NeT-Erlangen collaboration
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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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