AKPIK 2: AKPIK II: Deep Learning
Mittwoch, 17. März 2021, 16:00–18:15, AKPIKa
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16:00 |
AKPIK 2.1 |
Demonstrating learned tree reconstruction with graph neural networks — James Kahn, Oskar Taubert, •Ilias Tsaklidis, Markus Götz, Giulio Dujany, Tobias Boeckh, Florian Bernlochner, Pablo Goldenzweig, Isabelle Ripp-Baudot, Lea Reuter, and Arthur Thaller for the Belle II collaboration
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16:15 |
AKPIK 2.2 |
Deep Learning Based Analysis Approaches in Radio Interferometry — •Kevin Schmidt, Felix Geyer, Stefan Fröse, and Paul-Simon Blomenkamp
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16:30 |
AKPIK 2.3 |
Deep Learning based Likelihood Reconstruction of IACT Events — •Noah Biederbeck
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16:45 |
AKPIK 2.4 |
Boosting the performance of the neural network using symmetry properties for the prediction of the shower maximum using the water Cherenkov Detectors of the Pierre Auger Observatory as an example — Darko Veberic, David Schmidt, Markus Roth, •Steffen Hahn, Ralph Engel, and Brian Wundheiler for the Pierre Auger collaboration
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17:00 |
AKPIK 2.5 |
Belle II pixeldetector cluster analyses using neural network algorithms — •Stephanie Käs, Jens Sören Lange, Katharina Dort, Marvin Peter, Irina Heinz, Johannes Bilk, Peter Lehnhardt, and Johannes Budak
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17:15 |
AKPIK 2.6 |
Event reconstruction in JUNO-TAO using Deep Learning — •Vidhya Thara Hariharan
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17:30 |
AKPIK 2.7 |
Kinematic Analysis of Radio Jets with Deep Learning — •Paul-Simon Blomenkamp and Kevin Schmidt
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17:45 |
AKPIK 2.8 |
A Neural Network Architecture for Radio Imaging — •Stefan Fröse and Kevin Schmidt
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18:00 |
AKPIK 2.9 |
Evaluation of Interferometric Data Reconstructed by Neural Networks — •Felix Geyer and Kevin Schmidt
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