G
Gregor Kasieczka
Researcher at University of Hamburg
Publications - 31
Citations - 1415
Gregor Kasieczka is an academic researcher from University of Hamburg. The author has contributed to research in topics: Autoencoder & Calorimeter (particle physics). The author has an hindex of 13, co-authored 31 publications receiving 1050 citations. Previous affiliations of Gregor Kasieczka include Heidelberg University & École Polytechnique Fédérale de Lausanne.
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Journal ArticleDOI
Deep-learning Top Taggers or The End of QCD?
TL;DR: In this article, a convolutional neural network was used to identify top quarks in Monte Carlo simulations of the Standard Model production channel and compared its performance to a multivariate QCD-based top tagger.
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QCD or What
TL;DR: This work shows how adversarial autoencoder networks, trained only on QCD jets, identify jets from decays of arbitrary heavy resonances using 4-vectors, allowing for a general and at the same time easily controllable search for new physics.
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Deep-learned Top Tagging with a Lorentz Layer
TL;DR: In this article, a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric, is introduced.
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The Machine Learning Landscape of Top Taggers
Gregor Kasieczka,Tilman Plehn,Anja Butter,Kyle Cranmer,Dipsikha Debnath,Barry M. Dillon,Malcolm Fairbairn,Darius A. Faroughy,Wojtek Fedorko,Loukas Gouskos,Jernej F. Kamenik,Patrick T. Komiske,Simon Leiss,Alison Lister,Sebastian Macaluso,Sebastian Macaluso,Eric M. Metodiev,Liam Moore,Benjamin Philip Nachman,Benjamin Philip Nachman,Karl Nordström,Jannicke Pearkes,Huilin Qu,Yannik Rath,Marcel Rieger,David Shih,Jennifer M. Thompson,Sreedevi Varma +27 more
TL;DR: In this article, a wide range of modern machine learning approaches were compared on the established task of identifying boosted, hadronically decaying top quarks, and they found that these new approaches are extremely powerful and great fun.
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Resonance searches with an updated top tagger
TL;DR: In this article, the authors studied the production and decay of a heavy gauge boson in the upcoming LHC run and proposed a new HEPTopTagger2, which includes an optimal choice of the size of the fat jet, N-subjettiness, and different modes of Qjets.