加速器中的粒子探测器测量的运动学特性数据集

Data Set Information: 数据是使用蒙特卡罗模拟生成的。前21个特征（第2-22列）是由加速器中的粒子探测器测量的运动学特性。最后七个功能是前21个功能的功能；这些是物理学家用来区分这两个类别的高级特征。人们有兴趣使用深度学习方法来避免物理学家手动开发此类特征的需要。原始文件中给出了使用标准物理包中的贝叶斯决策树和5层神经网络的基准测试结果。最后500000个示例用作测试集。 Attribute Information: The first column is the class label (1 for signal, 0 for background), followed by the 28 features (21 low-level features then 7 high-level features): lepton pT, lepton eta, lepton phi, missing energy magnitude, missing energy phi, jet 1 pt, jet 1 eta, jet 1 phi, jet 1 b-tag, jet 2 pt, jet 2 eta, jet 2 phi, jet 2 b-tag, jet 3 pt, jet 3 eta, jet 3 phi, jet 3 b-tag, jet 4 pt, jet 4 eta, jet 4 phi, jet 4 b-tag, m_jj, m_jjj, m_lv, m_jlv, m_bb, m_wbb, m_wwbb. For more detailed information about each feature see the original paper. Relevant Papers: Baldi, P., P. Sadowski, and D. Whiteson. “Searching for Exotic Particles in High-energy Physics with Deep Learning.” Nature Communications 5 (July 2, 2014). Citation Request: Baldi, P., P. Sadowski, and D. Whiteson. “Searching for Exotic Particles in High-energy Physics with Deep Learning.” Nature Communications 5 (July 2, 2014). Daniel Whiteson daniel '@' uci.edu, Assistant Professor, Physics & Astronomy, Univ. of California Irvine