T 21: Data analysis, Information technology I
Monday, March 15, 2021, 16:00–18:15, Tu
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16:00 |
T 21.1 |
Anomaly searches for new physics based on generative classifiers — •Sven Bollweg and Gregor Kasieczka
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16:15 |
T 21.2 |
Searching for new physics with anomaly detection — •Manuel Sommerhalder, Tobias Lösche, Gregor Kasieczka, David Shih, and Anna Hallin
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16:30 |
T 21.3 |
Utilization of GPUs in the training of neural networks — Günter Quast, Roger Wolf, Janek Bechtel, Sebastian Brommer, Rene Caspart, Ralf Schmieder, Felix Heyen, Gessi Risto, Andrew Issac, and •Tim Voigtländer
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16:45 |
T 21.4 |
Improved energy resolution via super-resolution — Johannes Erdmann, Florian Mentzel, Olaf Nackenhorst, and •Aaron van der Graaf
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17:00 |
T 21.5 |
Decoding γ-showers: Physics in the Latent Space of a BIB-AE Generative Network — •Erik Buhmann
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17:15 |
T 21.6 |
Hadronic Shower Separation in Five Dimensions using Machine Learning Methods — •Jack Rolph, Gregor Kasieczka, and Erika Garutti
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17:30 |
T 21.7 |
Applications of Graph Neural Networks in Liquid Scintillator Neutrino Detectors — •Alexandros Tsagkarakis, Markus Bachlechner, Thilo Birkenfeld, Philipp Soldin, Achim Stahl, and Christopher Wiebusch
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17:45 |
T 21.8 |
Online Event Selection using GPUs for the Mu3e experiment — •Valentin Henkys for the Mu3e collaboration
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18:00 |
T 21.9 |
Muon Track Reconstruction in Liquid Scintillators with Graph Neural Networks — •Rosmarie Wirth
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