T 77: Deep Learning III
Donnerstag, 28. März 2019, 16:00–18:15, H06
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16:00 |
T 77.1 |
Studies of Energy Reconstruction with Deep Learning at the LHC — •Simon Schnake, Hartmut Stadie, and Peter Schleper
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16:15 |
T 77.2 |
Deep Learned Calorimetry with the CALICE AHCAL Technological Prototype — •Erik Buhmann and Gregor Kasieczka for the CALICE-D collaboration
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16:30 |
T 77.3 |
A Neural Network Approach to Estimate the Mass of Resonances decaying to τ+τ− with the ATLAS Detector — •Martin Werres, Philip Bechtle, Klaus Desch, Christian Grefe, Michael Hübner, Lara Schildgen, and Peter Wagner
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16:45 |
T 77.4 |
Investigation of the top-quark mass precision using machine-learning techniques at the ATLAS experiment — •Steffen Ludwig, Andrea Knue, and Gregor Herten
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17:00 |
T 77.5 |
A deep learning based search for a heavy CP-even Higgs boson in dileptonic H → WW decays with the CMS experiment — •Peter Fackeldey and Dennis Roy
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17:15 |
T 77.6 |
ttγ topology training through neural network — •Binish Batool
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17:30 |
T 77.7 |
Trennung von Signal und Untergrund in ttγ-Prozessen durch Nutzung eines neuronalen Netzes in leptonischen Endzuständen bei √s = 13 TeV in ATLAS — •Steffen Korn, Thomas Peiffer, Arnulf Quadt, Elizaveta Shabalina, Royer Edson Ticse Torres und Knut Zoch
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17:45 |
T 77.8 |
A DeepWWTagger for CMS — Paolo Gunnellini, Johannes Haller, Roman Kogler, and •Andrea Malara
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18:00 |
T 77.9 |
Multi-Class Boosted Object Tagger for Reclustered Jets at the ATLAS Experiment — •Elena Freundlich, Olaf Nackenhorst, Johannes Erdmann, and Kevin Kröninger
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