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Training, generation, and analysis code for Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics

Home Page: https://arxiv.org/abs/1701.05927

License: Other

Makefile 0.07% Python 5.73% Jupyter Notebook 94.20%
cern deep-learning gan generative-adversarial-network hep high-energy-physics machine-learning physics physics-simulation

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adversarial-jets's Issues

To-do

  • We need to not train on the whole 2M sample (too long to experiment). Note that these samples are unshuffled and we need to shuffle them as there is a big block of signal and a big block of bkg.
  • We should convert all instances of U[-1, 1] noise to N(0, 1) noise
  • We should make some plotting code to evaluate after training. Maybe start with trying to make some plots for the average jets that look like the ones in the paper. There is some code for plotting average / individual jets here.

Add other architectures to models/

In the arXiv paper, we say that we tried different things along the way (FCN, CNN, 2-stream, etc.) and that although we don't report the results for all, the architectures are available on github. We should add them to the models/ folder.

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