AKPIK 3: Machine Learning in Particle- and Astroparticle Physics
Thursday, April 3, 2025, 16:15–17:30, Theo 0.134
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16:15 |
AKPIK 3.1 |
A Hybrid Approach for Optimizing Background Simulations in IceCube — •Simon Koch, Christian Haack, and Benedikt Mayer
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16:30 |
AKPIK 3.2 |
Searching for Ultra-High Energy Photons applying Machine Learning Methods Using the Surface Detector of the Pierre Auger Observatory — •Fiona Ellwanger for the Pierre-Auger collaboration
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16:45 |
AKPIK 3.3 |
Neural Network-Based Event-by-Event Reconstruction of Muon Number from Data of the SD-750 of the Pierre Auger Observatory — •Alina Klingel for the Pierre-Auger collaboration
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17:00 |
AKPIK 3.4 |
Advanced Northern Tracks Selection using a Graph Convolutional Neural Network for the IceCube Neutrino Observatory: Adversarial Training — •Leon Hamacher, Philipp Behrens, Jakob Böttcher, Shuyang Deng, Lasse Düser, Philipp Fürst, Philipp Soldin, and Christopher Wiebusch for the IceCube collaboration
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17:15 |
AKPIK 3.5 |
Adaptive Generative Modeling for Accelerated Calorimeter Simulations via Domain Transfer — •Lorenzo Valente, Fank Gaede, Gregor Kasieczka, and Anatolii Korol
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