The Machine Learning Landscape of Top Taggers
作者： Gregor Kasieczka , Tilman Plehn , Anja Butter , Kyle Cranmer , Dipsikha Debnath
DOI: 10.21468/SCIPOSTPHYS.7.1.014
关键词:
摘要: Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range modern machine learning approaches. Unlike most methods they rely low-level input, for instance calorimeter output. While their network architectures are vastly different, performance is comparatively similar. In general, find that these new approaches extremely powerful and great fun.
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