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

Last update: Oct 22, 2022

Overview

Location-Aware Generative Adversarial Networks (LAGAN) for Physics Synthesis

This repository contains all the code used in L. de Oliveira (@lukedeo), M. Paganini (@mickypaganini), B. Nachman (@bnachman), Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis [arXiv:1701.05927]

Citations

You are more than welcome to use the open data and open-source software provided here for any of your projects, but we kindly ask you that you please cite them using the DOIs provided below:

Asset	Location
Source Code (this repository)
Data (Pythia images)
Model Weights
Docker image (generation)

If you're using ideas or methods discussed in the paper, with or without using the software, please cite:

@article{lagan,
      author         = "de Oliveira, Luke and Paganini, Michela and Nachman, Benjamin",
      title          = "{Learning Particle Physics by Example: Location-Aware
                        Generative Adversarial Networks for Physics Synthesis}",
      year           = "2017",
      eprint         = "1701.05927",
      archivePrefix  = "arXiv",
      primaryClass   = "stat.ML",
      SLACcitation   = "%%CITATION = ARXIV:1701.05927;%%"
}

Getting Started

To clone everything necessary, you'll need to run git clone --recursive https://github.com/lukedeo/adversarial-jets to fetch all the submodules (you can add a -j6 or some other number to launch concurrent clones).

This repository is structured as such:

generation
models
analysis

Generation

[TODO: allow for preprocessing in Docker] This folder links to the submodule used for generating Pythia images.

Models

This folder contains the Keras models used for training the LAGAN seen in the paper. By running python train.py -h from this folder, you should see all available options for running the training, as well as how to find / download the data required.

Analysis

[TODO: update filenames, download links, etc.] This folder contains a jupyter nootbook that will guide you through the production of the plots that appear in the paper. You will be able to reproduce them and modify them as you wish using our trained models and open datasets, or reuse the plotting functions to visualize the performance of your own LAGAN.

Simply run: jupyter notebook plots.ipynb

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Comments

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.

opened by mickypaganini 1
Link github with Zenodo for code citation

This is something I think only @lukedeo can do, given that the repo is under his name. Here are the instructions: https://guides.github.com/activities/citable-code/

opened by mickypaganini 1
To-do
[x] 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.

[x] We should convert all instances of U[-1, 1] noise to N(0, 1) noise

[x] 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.
opened by lukedeo 0

Releases(0.1.0)

0.1.0(Mar 19, 2017)

Initial release of fully reproducible repository, with model weights available.
Source code(tar.gz)
Source code(zip)

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

Related tags

Overview

Location-Aware Generative Adversarial Networks (LAGAN) for Physics Synthesis

Citations

Getting Started

Generation

Models

Analysis

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