AKPIK 4: Deep Learning
Thursday, March 24, 2022, 16:15–18:30, AKPIK-H13
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16:15 |
AKPIK 4.1 |
Using Graph Neural Networks for improving Cosmic-Ray Composition Analysis at IceCube Observatory — •Paras Koundal for the IceCube collaboration
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16:30 |
AKPIK 4.2 |
Amplifying Calorimeter Simulations with Deep Neural Networks — •Sebastian Guido Bieringer, Anja Butter, Sascha Diefenbacher, Engin Eren, Frank Gaede, Daniel Hundshausen, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn, and Mathias Trabs
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16:45 |
AKPIK 4.3 |
Deep Learning-based Imaging in Radio Interferometry — •Felix Geyer and Kevin Schmidt
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17:00 |
AKPIK 4.4 |
Binary Black Hole Parameter Reconstruction using Deep Neural Networks — •Markus Bachlechner, David Bertram, and Achim Stahl
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17:15 |
AKPIK 4.5 |
A Recurrent Neural Network for Radio Imaging — •Stefan Fröse and Kevin Schmidt
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17:30 |
AKPIK 4.6 |
Measurement of the Mass Composition using the Surface Detector of the Pierre Auger Observatory and Deep Learning — Martin Erdmann, •Jonas Glombitza, and Niklas Langner for the Pierre Auger collaboration
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17:45 |
AKPIK 4.7 |
Graph Neural Networks for Low Energy Neutrino Reconstruction at IceCube — •Rasmus Ørsøe
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18:00 |
AKPIK 4.8 |
Event-by-event estimation of high-level observables with data taken by the Surface Detector of the Pierre Auger Observatory using deep neural networks — •Steffen Hahn, Markus Roth, Darko Veberic, David Schmidt, Ralph Engel, and Brian Wundheiler
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18:15 |
AKPIK 4.9 |
Reconstruction of primary particle energy from data taken by the Surface Detector of the Pierre Auger Observatory using deep neural networks — Ralf Engel, Markus Roth, Darko Veberic, David Schmidt, Steffen Hahn, and •Fiona Ellwanger for the Pierre Auger collaboration
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