ML4EFT documentation

Code

  • Installation
  • Tutorial
  • ml4eft
    • ml4eft.analyse
      • ml4eft.analyse.analyse
        • ml4eft.analyse.analyse.Analyse
      • ml4eft.analyse.animate
        • ml4eft.analyse.animate.Animate
    • ml4eft.core
      • ml4eft.core.classifier
        • ml4eft.core.classifier.Classifier
        • ml4eft.core.classifier.ConstraintActivation
        • ml4eft.core.classifier.CustomActivationFunction
        • ml4eft.core.classifier.EventDataset
        • ml4eft.core.classifier.Fitter
        • ml4eft.core.classifier.MLP
        • ml4eft.core.classifier.PreProcessing
      • ml4eft.core.th_predictions
        • ml4eft.core.th_predictions.TheoryPred
      • ml4eft.core.truth
        • ml4eft.core.truth.tt_prod
    • ml4eft.limits
      • ml4eft.limits.optimize_ns
        • ml4eft.limits.optimize_ns.Optimize
    • ml4eft.plotting
      • ml4eft.plotting.features
        • ml4eft.plotting.features.plot_features
    • ml4eft.preproc
      • ml4eft.preproc.lhe_reader
        • ml4eft.preproc.lhe_reader.get_deta
        • ml4eft.preproc.lhe_reader.get_dphi
        • ml4eft.preproc.lhe_reader.Kinematics

Results

  • Unbinned multivariate observables for global SMEFT analyses from machine learning
    • Inclusive top pair production at the parton level
      • Pseudo-data generation and benchmarking
      • Neural network training
      • Constraints on the SMEFT
    • Inclusive top pair production at the particle level
      • Kinematic features used as inputs to the neural network
      • Results from the ML model vs binning in two features, \(O(\Lambda^{-2})\)
      • Results from the ML model trained on two features vs all features, \(O(\Lambda^{-2})\)
      • Results from the ML model vs binning in two features, \(O(\Lambda^{-4})\)
      • Results from the ML model training on two features vs all features, \(O(Λ^{-4})\)
      • Impact of methodological uncertainties
    • Higgs production in association with a Z boson
      • Kinematic features used as inputs to the neural network
      • Results from the ML model vs binning in \(p_{T}^{Z}\) , \(O(\Lambda^{-2})\) , pair-wise fit
      • Results from the ML model vs binning in \(p_{T}^{Z}\) , \(O(\Lambda^{-4})\) , pair-wise fit
      • Results from the ML model vs binning in \(p_{T}^{Z}\) , \(O(\Lambda^{-4})\) , fully marginalised
      • Impact of methodological uncertainties

Bibliography

  • Bibliography
Theme by the Executable Book Project

Python Module Index

m
 
m
- ml4eft
    ml4eft.analyse
    ml4eft.analyse.analyse
    ml4eft.analyse.animate
    ml4eft.core
    ml4eft.core.classifier
    ml4eft.core.th_predictions
    ml4eft.core.truth
    ml4eft.core.truth.tt_prod
    ml4eft.limits
    ml4eft.limits.optimize_ns
    ml4eft.plotting
    ml4eft.plotting.features
    ml4eft.preproc
    ml4eft.preproc.lhe_reader

By R. Gomez Ambrosio, J. ter Hoeve, M. Madigan, J. Rojo, V. Sanz
© Copyright 2022, ML4EFT developer team.