T 21: Experimentelle Methoden 1 (Computing, Machine Learning, Statistik)
Montag, 27. März 2017, 16:45–19:00, JUR 253
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16:45 |
T 21.1 |
Design and Execution of make-like Distributed Analyses — •Robert Fischer, Ralf Florian von Cube, Martin Erdmann, Benjamin Fischer, and Marcel Rieger
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17:00 |
T 21.2 |
Aktuelle Entwicklungen des Meta-Monitoring Frameworks HappyFace — •Artur Il'Darovic Akhmetshin, Sebastian Brommer, Manuel Giffels, Georg Sieber und Günter Quast
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17:15 |
T 21.3 |
First steps towards an improved tuning method for Monte Carlo generators — •Fabian Klimpel, Stefan Kluth, and Andrea Knue
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17:30 |
T 21.4 |
Application of the VISPA web-platform for deep-learning based physics analyses — •Benjamin Fischer, Martin Erdmann, Robert Fischer, Erik Geiser, Christian Glaser, Gero Müller, Thorben Quast, Marcel Rieger, Martin Urban, Florian von Cube, David Walz, and Christoph Welling
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17:45 |
T 21.5 |
Development of morphing algorithms for Histfactory using information geometry — •Anjishnu Bandyopadhyay, Ian Brock, and Kyle Cranmer
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18:00 |
T 21.6 |
Multivariate Regression on the Example of Missing Transverse Energy Estimation — •Nicola Zäh, Raphael Friese, Günther Quast, and Roger Wolf
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18:15 |
T 21.7 |
Konfidenzintervalle und Ausschlussgrenzen am Beispiel der Analyse des Verzweigungsverhältnisses von Bs0→µµµµ — Johannes Albrecht, •Titus Mombächer, Stefanie Reichert und Konstantin Schubert für die LHCb Kollaboration
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18:30 |
T 21.8 |
Kontinuumsunterdrückung mit Deep Learning Techniken für das Belle II-Experiment — •Dennis Weyland, Michael Feindt, Jochen Gemmler, Pablo Goldenzweig, Thomas Hauth, Martin Heck und Thomas Keck
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18:45 |
T 21.9 |
Modern Machine Learning Methods in HEP — Raphael Friese, Günter Quast, Roger Wolf, and •Stefan Wunsch
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