SUSY

Donated on 2/11/2014

This is a classification problem to distinguish between a signal process which produces supersymmetric particles and a background process which does not.

Dataset Characteristics

-

Subject Area

Physics and Chemistry

Associated Tasks

Classification

Feature Type

Real

# Instances

5000000

# Features

-

Dataset Information

Additional Information

Provide all relevant informatioThe data has been produced using Monte Carlo simulations. The first 8 features are kinematic properties measured by the particle detectors in the accelerator. The last ten features are functions of the first 8 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 and the dropout algorithm are presented in the original paper. The last 500,000 examples are used as a test set.n about your data set.

Has Missing Values?

No

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
no
no
no
no
no
no
no
no
no
no

0 to 10 of 18

Additional Variable Information

The first column is the class label (1 for signal, 0 for background), followed by the 18 features (8 low-level features then 10 high-level features):: lepton 1 pT, lepton 1 eta, lepton 1 phi, lepton 2 pT, lepton 2 eta, lepton 2 phi, missing energy magnitude, missing energy phi, MET_rel, axial MET, M_R, M_TR_2, R, MT2, S_R, M_Delta_R, dPhi_r_b, cos(theta_r1). For detailed information about each feature see the original paper.

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Creators

Daniel Whiteson

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