Würzburg 2018 –
wissenschaftliches Programm
T 87: Datenanalyse
Donnerstag, 22. März 2018, 16:30–19:00, Z6 - SR 2.005
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16:30 |
T 87.1 |
Adversarial networks used in a single-top-quark analysis in ATLAS — •Rui Zhang and Ian C. Brock
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16:45 |
T 87.2 |
Modernized track reconstruction in ATLAS with the ACTS software project — •Paul Gessinger, Andreas Salzburger, and Stefan Tapprogge
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17:00 |
T 87.3 |
Deep Learning mit unbalancierten Datensätzen — •Stefan Geißelsöder für die ANTARES-KM3NeT-Erlangen Kollaboration
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17:15 |
T 87.4 |
Distributed make-like Analyses on the Grid based on Spotify's Pipelining Package luigi — •Marcel Rieger, Martin Erdmann, Benjamin Fischer, and Ralf Florian von Cube
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17:30 |
T 87.5 |
KM3NeT/ORCA data analysis using unsupervised Deep Learning — •Stefan Reck for the ANTARES-KM3NeT-Erlangen collaboration
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17:45 |
T 87.6 |
Jet-Rekonstruktion mit neuronalen Netzen im ATLAS Level-1 Kalorimeter Trigger — •Bastian Schlag, Volker Büscher, Christian Schmitt, Stefan Kramer und Andreas Karwath
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18:00 |
T 87.7 |
The contribution has been withdrawn.
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18:15 |
T 87.8 |
Studies for Top Quark Reconstruction with Deep Learning — •Tim Kallage, Johannes Erdmann, Olaf Nackenhorst, and Kevin Kröninger
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18:30 |
T 87.9 |
Jet-Klassifizierung mithilfe von „domain adaption“ in tiefen künstlichen neuronalen Netzen — Matthias Mozer, Thomas Müller und •David Walter
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18:45 |
T 87.10 |
Tau neutrino appearance studies with KM3NeT-ORCA using Deep Learning techniques — •Michael Moser for the ANTARES-KM3NeT-Erlangen collaboration
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