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higgs

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.

Features

FeaturesDict({
    'class_label': Tensor(shape=(), dtype=tf.float32),
    'jet_1_b-tag': Tensor(shape=(), dtype=tf.float64),
    'jet_1_eta': Tensor(shape=(), dtype=tf.float64),
    'jet_1_phi': Tensor(shape=(), dtype=tf.float64),
    'jet_1_pt': Tensor(shape=(), dtype=tf.float64),
    'jet_2_b-tag': Tensor(shape=(), dtype=tf.float64),
    'jet_2_eta': Tensor(shape=(), dtype=tf.float64),
    'jet_2_phi': Tensor(shape=(), dtype=tf.float64),
    'jet_2_pt': Tensor(shape=(), dtype=tf.float64),
    'jet_3_b-tag': Tensor(shape=(), dtype=tf.float64),
    'jet_3_eta': Tensor(shape=(), dtype=tf.float64),
    'jet_3_phi': Tensor(shape=(), dtype=tf.float64),
    'jet_3_pt': Tensor(shape=(), dtype=tf.float64),
    'jet_4_b-tag': Tensor(shape=(), dtype=tf.float64),
    'jet_4_eta': Tensor(shape=(), dtype=tf.float64),
    'jet_4_phi': Tensor(shape=(), dtype=tf.float64),
    'jet_4_pt': Tensor(shape=(), dtype=tf.float64),
    'lepton_eta': Tensor(shape=(), dtype=tf.float64),
    'lepton_pT': Tensor(shape=(), dtype=tf.float64),
    'lepton_phi': Tensor(shape=(), dtype=tf.float64),
    'm_bb': Tensor(shape=(), dtype=tf.float64),
    'm_jj': Tensor(shape=(), dtype=tf.float64),
    'm_jjj': Tensor(shape=(), dtype=tf.float64),
    'm_jlv': Tensor(shape=(), dtype=tf.float64),
    'm_lv': Tensor(shape=(), dtype=tf.float64),
    'm_wbb': Tensor(shape=(), dtype=tf.float64),
    'm_wwbb': Tensor(shape=(), dtype=tf.float64),
    'missing_energy_magnitude': Tensor(shape=(), dtype=tf.float64),
    'missing_energy_phi': Tensor(shape=(), dtype=tf.float64),
})

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Urls

Citation

@article{Baldi:2014kfa,
      author         = "Baldi, Pierre and Sadowski, Peter and Whiteson, Daniel",
      title          = "{Searching for Exotic Particles in High-Energy Physics
                        with Deep Learning}",
      journal        = "Nature Commun.",
      volume         = "5",
      year           = "2014",
      pages          = "4308",
      doi            = "10.1038/ncomms5308",
      eprint         = "1402.4735",
      archivePrefix  = "arXiv",
      primaryClass   = "hep-ph",
      SLACcitation   = "%%CITATION = ARXIV:1402.4735;%%"
}