Falcon

Using machine learning to build a fast detector simulator that maps parton jets to reco-jets. This project uses data generated here.

Requirements

• Root (tested on version 6.22.2)
• FastJet (tested on version 3.3.4)

Other requirments listed in requirements.txt.

Using the makefile requires the following environmental variables to be set (if you ran source bin/thisroot.sh in your root installation or installed root in an active conda environment, ROOTSYS has already been set).

export ROOTSYS=path/to/your/root/installation 
export FASTJETSYS=path/to/your/fastJet/installation

Use

In addition to the existing directories, add the following empty directories

falcon
│
└───data
|   └───plots
│   └───processed
│   └───raw
└───bin

Analyzing data

There are two programs that analyze data. Make sure the output root file from here is located in /falcon/data/raw, and that the name of this file is events.root. To match the parton, reco and gen jets in the root file, run make makeHistos.out and then make histos. A root file containing various histograms relating to the matching will be in /falcon/data/plots/histos.root. To write out the 4-momenta of matched parton and reco jets to be used later for machine learning, run make writeJetMomenta.out and then make data, which will write the 4-momenta of the matched jets to /falcon/data/processed/matchedJets.txt.

Training

There are currently three machine learning models that have been used for this project. A fully connected network, a conditional GAN, and a conditional Wasserstein GAN. To train a model, move to the /falcon/src/learning/ directory. In the directory there is a train.py script, as well as a example.json file in which training/model hyperparameters are set. The train.py takes two arguments, a choice of model (either FCNN, cGAN, or cWGAN), and the name of the configuration file. To train the cWGAN, for example, run python train.py cWGAN example.json. This will create the directry /falcon/models/cWGAN/Run_<todays date>_0/ where the losses and weights will be saved.

Putting it all together

After cloning the repo, importing the root file produced from here and creating the directories as described above, running the code below would cluster the parton jets, match parton and reco jets, and then train a conditional GAN to generate realistic reco jet 4-momenta given a parton jet 4-momentum.

make writeJetMomenta.out
make data
cd src/learning/
python train.py cWGAN example.json