Aachen 2019 –
scientific programme
T 4: Deep Learning I
Monday, March 25, 2019, 16:00–18:30, H06
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16:00 |
T 4.1 |
Physics inspired feature engineering with Lorentz Boost Networks — •Yannik Rath, Martin Erdmann, Erik Geiser, and Marcel Rieger
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16:15 |
T 4.2 |
Further development of the ATLAS Deep Learning flavour tagging algorithm — •Manuel Guth
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16:30 |
T 4.3 |
Application of Deep Learning to Heavy Flavour Jet Identification with the CMS Experiment — Xavier Coubez, Luca Mastrolorenzo, •Spandan Mondal, Andrzej Novak, Andrey Pozdnyakov, and Alexander Schmidt
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16:45 |
T 4.4 |
Validation of a Deep Neural Network Based Flavor Tagging Algorithm at Belle and Belle II — •Jochen Gemmler, Florian Bernlochner, Michael Feindt, and Pablo Goldenzweig for the Belle 2 collaboration
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17:00 |
T 4.5 |
Adversarial Neural Network-based data-simulation corrections for jet-tagging at CMS — Martin Erdmann, •Benjamin Fischer, Dennis Noll, Yannik Rath, Marcel Rieger, and David Schmidt
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17:15 |
T 4.6 |
Hyperparameter optimization of Adversarial Neural Networks in the tW dilepton channel using the ATLAS detector — •Christian Kirfel, Ian Brock, and Rui Zhang
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17:30 |
T 4.7 |
Adversarial Neural Networks zur Reduzierung des Einflusses von systematischen Unsicherheiten am Beispiel einer ttH -Analyse — •Jörg Schindler, Karim El Morabit, Ulrich Husemann, Philip Keicher, Matthias Schröder, Jan van der Linden und Michael Wassmer
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17:45 |
T 4.8 |
Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network — Martin Erdmann, Jonas Glombitza, and •Thorben Quast
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18:00 |
T 4.9 |
Parton showers with Generative Adversarial Networks — •Christof Sauer
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18:15 |
T 4.10 |
Reinforced Sorting Networks for Particle Physics Analyses — Martin Erdmann, Benjamin Fischer, •Dennis Noll, Yannik Alexander Rath, Marcel Rieger, David Josef Schmidt, and Marcus Wirtz
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