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Aachen 2019 – scientific programme

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T: Fachverband Teilchenphysik

T 77: Deep Learning III

Thursday, March 28, 2019, 16:00–18:15, H06

16:00 T 77.1 Studies of Energy Reconstruction with Deep Learning at the LHC — •Simon Schnake, Hartmut Stadie, and Peter Schleper
16:15 T 77.2 Deep Learned Calorimetry with the CALICE AHCAL Technological Prototype — •Erik Buhmann and Gregor Kasieczka for the CALICE-D collaboration
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
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
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
17:15 T 77.6 ttγ topology training through neural network — •Binish Batool
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
17:45 T 77.8 A DeepWWTagger for CMSPaolo Gunnellini, Johannes Haller, Roman Kogler, and •Andrea Malara
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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