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Code repository for the paper "Constraining Effective Field Theories with Machine Learning"

Johann Brehmer, Kyle Cranmer, Gilles Louppe, and Juan Pavez

Note: If you want to use these methods for a particle physics problem, please have a look at our new tool MadMiner -- you will likely find that much easier to use than this repo!

Folder structure

  • cluster_scripts: SLURM scripts that start the preprocessing and experiments on the NYU HPC cluster.
  • data: The data set, including both the original weighted event sample as well as the unweighted training, calibration, and evaluation samples. Some of these are quite large and not on GitHub.
  • evaluation: IPython notebooks that extract metrics and figures from the experiments.
  • figures: Here the figures are stored.
  • higgs_inference: The main folder for the inference experiments.
    • models: The keras model code at the heart of many inference strategies.
    • strategies: Training and evaluation routines for the different inference strategies.
    • various: Different utility functions.
    • Main executable that starts the different training and evaluation pieces.
    • Most settings and constants, including the main directories, event numbers, architecture parameters, and benchmark thetas.
  • preprocessing: Unweighting routines that generate the different training, calibration, and evaluation samples from the original weighted event file.
  • postprocessing: Code for the Neyman construction.
  • prototypes: Toy experiments and cross-checks on other data. Includes the flow folder with a PyTorch implementation of normalizing flows.
  • results: The predictions of the different algorithms on the test data set.


Code repository for the paper "Constraining Effective Field Theories with Machine Learning"



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