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Source: Daniel Whiteson daniel '@' uci.edu, Assistant Professor, Physics & Astronomy, Univ. of California Irvine Data Set Information: Machine learning is used in high-energy physics experiments to search for the signatures of exotic particles. These signatures are learned from Monte Carlo simulations of the collisions that produce these particles and the resulting decay products. In each of the three data sets here, the goal is to separate particle-producing collisions from a background source.
Attribute Information: The first column is the class label (1 for signal, 0 for background), followed by the 27 normalized features (22 low-level features then 5 high-level features), and a 28th mass feature for datasets 2 and 3. See the original paper for more detailed information.
Relevant Papers: Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, and Daniel Whiteson. 'Parameterized Machine Learning for High-Energy Physics.' In submission. Citation Request: If you have no special citation requests, please leave this field blank. |
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