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SUSY Data Set
Download: Data Folder, Data Set Description

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

Data Set Characteristics:  

N/A

Number of Instances:

5000000

Area:

Physical

Attribute Characteristics:

Real

Number of Attributes:

18

Date Donated

2014-02-12

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

31009


Source:

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


Data Set 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.


Attribute 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.


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)



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)


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