higgs
OpenML dataset with id 4532
Assistant Professor, Daniel Whiteson daniel '@' uci.edu, Univ. of California Irvine, Physics
Full work available at URL: https://api.openml.org/data/v1/download/1798100/higgs.arff
Upload date: 16 February 2016
Dataset Characteristics
Number of classes: 0
Number of features: 29 (numeric: 29, symbolic: 0 and in total binary: 0 )
Number of instances: 98,050
Number of instances with missing values: 1
Number of missing values: 9
Author: Daniel Whiteson daniel'@'uci.edu", Assistant Professor, Physics, Univ. of California Irvine Source: UCI Please cite: Baldi, P., P. Sadowski, and D. Whiteson. Searching for Exotic Particles in High-energy Physics with Deep Learning. Nature Communications 5 (July 2, 2014).
Data Set Information:
The data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate the need for physicists to manually develop such features. Benchmark results using Bayesian Decision Trees from a standard physics package and 5-layer neural networks are presented in the original paper. The last 500,000 examples are used as a test set.
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).
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