Files:

HIGGS.csv.gz --- CSV file (columns described below).

HIGGS.h5 --- HDF5 file containing same data as HIGGS.csv.gz. 

Source:

Daniel Whiteson daniel '@' uci.edu, Assistant Professor, Physics & Astronomy,
Univ. of California Irvine


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.


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).

This data is also available from the HIGGS and SUSY datasets on the UCI Machine Learning repository.

https://archive.ics.uci.edu/ml/datasets/HIGGS

https://archive.ics.uci.edu/ml/datasets/SUSY