HIGGS
Donated on 2/11/2014
This is a classification problem to distinguish between a signal process which produces Higgs bosons and a background process which does not.
Dataset Characteristics
-
Subject Area
Physics and Chemistry
Associated Tasks
Classification
Feature Type
Real
# Instances
11000000
# Features
-
Dataset Information
Additional 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.
Has Missing Values?
No
Introductory Paper
By P. Baldi, Peter Sadowski, D. Whiteson. 2014
Published in Nature Communications
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no |
0 to 10 of 28
Additional Variable 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.
Dataset Files
| File | Size |
|---|---|
| HIGGS.csv.gz | 2.6 GB |
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset higgs = fetch_ucirepo(id=280) # data (as pandas dataframes) X = higgs.data.features y = higgs.data.targets # metadata print(higgs.metadata) # variable information print(higgs.variables)
Whiteson, D. (2014). HIGGS [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5V312.
Creators
Daniel Whiteson
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.