Erlangen 2026 –
wissenschaftliches Programm
T 28: Data, AI, Computing, Electronics III
Dienstag, 17. März 2026, 16:15–18:45, KH 00.024
 |
16:15 |
T 28.1 |
Multi-Modal track reconstruction using Graph Neural Networks at Belle II — •Tristan Brandes, Torben Ferber, Giacomo De Pietro, and Lea Reuter
|
|
|
|
16:30 |
T 28.2 |
Graph Neural Networks for multi-hypothesis clustering in the Belle II Electromagnetic Calorimeter to improve hadron clustering — •Jonas Eppelt and Torben Ferber
|
|
|
|
16:45 |
T 28.3 |
Point Cloud Segmentation for the Belle II GNN-Based Tracking — •Daniel Grossmann, Tristan Brandes, Giacomo De Pietro, Torben Ferber, and Lea Reuter
|
|
|
|
17:00 |
T 28.4 |
Graph Neural Network based inclusive flavour tagger at the LHCb experiment — •Yukai Zhao, Sara Celani, Stephanie Hansmann-Menzemer, and Pelian Li
|
|
|
|
17:15 |
T 28.5 |
Machine-Learning based Energy Regression of Muon Detector Showers in CMS — •Mascha Hackmann, Ayse Asu Guvenli, Karim El Morabit, and Gregor Kasieczka
|
|
|
|
17:30 |
T 28.6 |
Machine-Learning Based Reconstruction of Muon Detector Showers in CMS — •Ayse Asu Guvenli, Karim El Morabit, Gregor Kasieczka, and Mascha Hackmann
|
|
|
|
17:45 |
T 28.7 |
Reconstructing missing transverse momentum for electroweak precision measurements at the ATLAS experiment — •Gabriel Sanchez Shestakova, Matthias Schott, Timo Saala, and Philip Bechtle
|
|
|
|
18:00 |
T 28.8 |
Secondary Particle Tracking with Graph Neural Networks for the ATLAS Experiment — •Hannah Schlenker, Sebastian Dittmeier, and André Schöning
|
|
|
|
18:15 |
T 28.9 |
Keeping Track of Graphs: 4D Tracking with Graph Neural Networks at Muon Colliders — •Lukas Bauckhage
|
|
|
|
18:30 |
T 28.10 |
Machine Learning Models for Separating Signal and Background Events in LHC pp Collisions — Oleksandr Shekhovtsov, André Sopczak, and •Lukas Vicenik
|
|
|
