Corpus ID: 221293337

Matrix Element Regression with Deep Neural Networks -- breaking the CPU barrier

  title={Matrix Element Regression with Deep Neural Networks -- breaking the CPU barrier},
  author={F. Bury and C. Delaere},
  journal={arXiv: High Energy Physics - Experiment},
  • F. Bury, C. Delaere
  • Published 2020
  • Physics
  • arXiv: High Energy Physics - Experiment
  • The Matrix Element Method (MEM) is a powerful method to extract information from measured events at collider experiments. Compared to multivariate techniques built on large sets of experimental data, the MEM does not rely on an examples-based learning phase but directly exploits our knowledge of the physics processes. This comes at a price, both in term of complexity and computing time since the required multi-dimensional integral of a rapidly varying function needs to be evaluated for every… CONTINUE READING
    1 Citations
    Matrix Element Method in the Machine Learning Era


    Parameterized neural networks for high-energy physics
    • 88
    • PDF
    Adam: A Method for Stochastic Optimization
    • 52,611
    • PDF
    Dropout: a simple way to prevent neural networks from overfitting
    • 19,997
    • PDF
    L2 Regularization for Learning Kernels
    • 207
    • PDF
    Deep Learning using Rectified Linear Units (ReLU)
    • 248
    • PDF
    Measurement of the top-quark mass with dilepton events selected using neuroevolution at CDF.
    • 65
    • PDF
    MoMEMta, a modular toolkit for the Matrix Element Method at the LHC
    • 4
    • PDF
    Test of analysis method for top—antitop production and decay events
    • R. Dalitz, G. Goldstein
    • Physics
    • Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences
    • 1999
    • 19
    • PDF