T 16: Data, AI, Computing 1 (anomaly detection)
Monday, March 4, 2024, 16:00–18:00, Geb. 30.33: MTI
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16:00 |
T 16.1 |
Anomaly Detection Using Autoencoders in Belle II Data — •David Giesegh, Nikolai Hartmann, and Thomas Kuhr
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16:15 |
T 16.2 |
VAE-based anomaly detection in dijet events at √s=13 TeV — •Aritra Bal, Benedikt Maier, Thea Aarrestad, Javier Duarte, Markus Klute, Jennifer Ngadiuba, Maurizio Pierini, Kinga Wozniak, and Irene Zoi
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16:30 |
T 16.3 |
Deep neural network reconstruction of muon densities from measurements of the underground muon detector of the Pierre Auger Observatory — •Anton Poctarev for the Pierre-Auger collaboration
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16:45 |
T 16.4 |
Neural network identification of highly inclined muons in water-Cherenkov particle detectors — •Mohsen Pourmohammad Shahvar for the Pierre-Auger collaboration
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17:00 |
T 16.5 |
Nested Machine Learning Models for the Cherenkov Telescope Array — Lukas Beiske and •Maximilian Linhoff for the CTA collaboration
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17:15 |
T 16.6 |
The contribution has been withdrawn.
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17:30 |
T 16.7 |
Machine-Learning-based Background Identification for the Radio Neutrino Observatory Greenland — •Philipp Laub for the RNO-G collaboration
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17:45 |
T 16.8 |
ANNs for enhanced Pulse Shape Discrimination in GERDA — •Vikas Bothe
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