T 29: Deep Learning II
Dienstag, 26. März 2019, 16:00–18:30, H06
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16:00 |
T 29.1 |
Reconstruction of Muons with Recurrent Neural Networks for the IceCube Experiment — •Gerrit Wrede, Gisela Anton, and Thorsten Glüsenkamp for the IceCube collaboration
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16:15 |
T 29.2 |
Antiproton to proton ratio determination using deep neural networks with AMS-02 — •Sichen Li
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16:30 |
T 29.3 |
Particle Identification using Deep Learning at AMS — •Robin Sonnabend
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16:45 |
T 29.4 |
Particle Discrimination via Deep Learning with JUNO — •Thilo Birkenfeld, Achim Stahl, and Christopher Wiebusch
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17:00 |
T 29.5 |
Search for Ultra High Energy Photons with the Pierre Auger Observatory using Deep Learning Techniques — •Tobias Pan, Thomas Bretz, Paulo Ferreira, Adrianna García, Thomas Hebbeker, Julian Kemp, and Christine Peters
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17:15 |
T 29.6 |
Signal-background discrimination with Deep Learning in the EXO-200 experiment — •Tobias Ziegler, Mike Jewell, Johannes Link, Federico Bontempo, Gisela Anton, and Thilo Michel
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17:30 |
T 29.7 |
Event Reconstruction with Machine Learning methods in JUNO — •Yu Xu, Yaping Cheng, Christoph Genster, Alexandre Göttel, Livia Ludhova, Philipp Kampmann, Michaela Schever, Achim Stahl, and Christopher Wiebusch
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17:45 |
T 29.8 |
Application of Deep Neural Networks to Event Type Classification in IceCube — •Maximilian Kronmüller and Theo Glauch for the IceCube collaboration
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18:00 |
T 29.9 |
Deep Learning based Air Shower Reconstruction at the Pierre Auger Observatory — •Jonas Glombitza, Martin Erdmann, Maximilian Vieweg, and Michael Dohmen
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18:15 |
T 29.10 |
Image Recognition with Deep Neural Networks for IceAct Air-Cherenkov Telescopes — •Matthias Thiesmeyer, Jan Auffenberg, Pascal Backes, Thomas Bretz, Erik Ganster, Maurice Günder, Merlin Schaufel, Jöran Stettner, and Christopher Wiebusch for the IceCube collaboration
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