T 7: Data, AI, Computing, Electronics I
Montag, 16. März 2026, 16:15–18:15, KH 00.024
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16:15 |
T 7.1 |
Understanding and Expanding the Transformer-Based Neural Network to Analyze Extensive Air Showers at the Pierre Auger Observatory — •Ronja Westphalen, Maximilian Straub, Alex Reuzki, Niklas Langner, Josina Schulte, and Martin Erdmann
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16:30 |
T 7.2 |
Reconstruction and Identification of Atmospheric Neutrino Events at JUNO Using Machine-Learning Methods — •Milo Charavet, Caren Hagner, Daniel Bick, and Mikhail Smirnov
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16:45 |
T 7.3 |
Particle Identification in OSIRIS using Deep Learning — •Martin Kandlen, Elisabeth Neuerburg, Achim Stahl und Christopher Wiebusch
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17:00 |
T 7.4 |
Self-Supervised Pretraining of HPGe Waveforms for Pulse-Shape Discrimination for LEGEND — •Niko Lay, Christoph Vogl, Tommaso Comellato, Konstantin Gusev, Brennan Hackett, Baran Hashemi, Lukas Heinrich, Patrick Krause, Andreas Leonhardt, Béla Majorovits, Moritz Neuberger, Nadezda Rumyantseva, Mario Schwarz, Michael Willers, and Stefan Schönert
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17:15 |
T 7.5 |
ML-based LAr classification in LEGEND-200 — •Jonas Schlegel and Christoph Wiesinger for the LEGEND collaboration
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17:30 |
T 7.6 |
Application of FiLM Neural Networks for π/K Separation in the PANDA Barrel DIRC — •Daniel Markhoff, Roman Dzhygadlo, Jochen Schwiening, and Yannic Wolf
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17:45 |
T 7.7 |
LSTM Networks for Enhanced Signal Efficiency in the CRESST Experiment — •Praveen Murali for the CRESST collaboration
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18:00 |
T 7.8 |
Machine learning based Particle Identification in a Diffusion Cloud Chamber. — •Benjamin Rosendahl, Jasper von Lepel, Mario Schwarz, and Stefan Schönert
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