The Machine Learning Landscape of Top Taggers

Not scheduled
32-123 (MIT)


Plenary Talk Session


Sebastian Macaluso (New York University)


Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. We find that they are extremely powerful and great fun.

Primary authors

Gregor Kasieczka (Hamburg University (DE)) Tilman Plehn Anja Butter Kyle Stuart Cranmer (New York University (US)) Dipsikha Debnath (University of Florida) Prof. Malcolm Fairbairn (Physics, King's College London) Wojtek Fedorko (University of British Columbia) Colin Warren Gay (University of British Columbia (CA)) Loukas Gouskos (CERN) Patrick Komiske (Massachusetts Institute of Technology) Simon Leiss Alison Lister (University of British Columbia (CA)) Sebastian Macaluso (New York University) Eric Metodiev (Massachusetts Institute of Technology) Liam Ronald Moore (Universite Catholique de Louvain (UCL) (BE)) Ben Nachman (Lawrence Berkeley National Lab. (US)) Dr Karl Nordstrom (LPTHE Paris) Jannicke Andree Pearkes (SLAC National Accelerator Laboratory (US)) Huilin Qu (Univ. of California Santa Barbara (US)) Yannik Alexander Rath (RWTH Aachen University (DE)) Marcel Rieger (RWTH Aachen University (DE)) David Shih (Rutgers University) Jennifer Thompson (ITP Heidelberg) Sreedevi Varma

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