Higgs
OpenML dataset with id 45570
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22116554/Higgs.arff
Upload date: 14 June 2023
Dataset Characteristics
Number of classes: 2
Number of features: 29 (numeric: 28, symbolic: 1 and in total binary: 1 )
Number of instances: 11,000,000
Number of instances with missing values: 0
Number of missing values: 0
This is a classification problem to distinguish between a signal process which produces Higgs bosons and a background process which does not.
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.
Notes by Uploader to OpenML
- This is the 11M version from UCI.
- This dataset is described in version 1 of arXiv:1402.4735 and version 2 on arXiv and the subsequent NeurIPS publication used larger versions of this dataset.
This page was built for dataset: Higgs
