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.
作者
暂无数据
相关技术
暂无数据
相关事件
暂无数据
相关文章
暂无数据