The Machine Learning landscape of top taggers 论文

2019SciPost Physics引用 236顶会
Particle physics theoretical and experimental studiesComputational Physics and Python ApplicationsQuantum Chromodynamics and Particle Interactions

详细信息

发表期刊/会议
SciPost Physics
发表日期
2019-07-30
发表年份
2019

关键词

Particle physics theoretical and experimental studiesComputational Physics and Python ApplicationsQuantum Chromodynamics and Particle Interactions

摘要

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.

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