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Aachen 2019 – scientific programme

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T: Fachverband Teilchenphysik

T 29: Deep Learning II

Tuesday, March 26, 2019, 16:00–18:30, H06

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
16:15 T 29.2 Antiproton to proton ratio determination using deep neural networks with AMS-02 — •Sichen Li
16:30 T 29.3 Particle Identification using Deep Learning at AMS — •Robin Sonnabend
16:45 T 29.4 Particle Discrimination via Deep Learning with JUNO — •Thilo Birkenfeld, Achim Stahl, and Christopher Wiebusch
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
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
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
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
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
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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