Mass Agnostic Jet Taggers
Layne Bradshaw, Rashmish K. Misha, Andrea Mitridate, and Bryan Ostdiek
This project explores the benefits of different methods to decorrelate the jet mass from a machine learning jet tagger. The paper can be found at [https://arxiv.org/abs/1908.08959]. If you use any of the results from this study, please cite:
@article{Bradshaw:2019ipy,
author = "Bradshaw, Layne and Mishra, Rashmish K. and Mitridate,
Andrea and Ostdiek, Bryan",
title = "{Mass Agnostic Jet Taggers}",
year = "2019",
eprint = "1908.08959",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
SLACcitation = "%%CITATION = ARXIV:1908.08959;%%"
}
Getting started
For reproducibility, we have included the environment we used. To generate this environment (conda is required)
Environment
make create_environment
conda activate massagnosticjettaggers
Data
All of the preprocessing of the data can be done with make data
Models
The models have already been trained, and the training takes quite a bit of time. However, they can be retrained using
make base_nn
make uBoost
make BDT
make planed_nn
make planed_bdt
make pca_nn
make pca_bdt
Predictions and Metrics
The predictions for the test data can be made using make predictions. After the predictions are made, the metrics are computed with make metrics.
Project Organization
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── interim <- Intermediate data that has been transformed.
│ ├── modelprediction <- The final, canonical data sets
│ └── raw <- The original, immutable data dump.
│
│
├── models <- Trained and serialized models and histories
│ ├── adv <- Adversarial trained networks
│ └── histories <- Adversarial training histories
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `01-bo-CheckScaling.ipynb`.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── environment.yml <- The requirements file for reproducing the analysis environment
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
└── src <- Source code for use in this project.
├── __init__.py <- Makes src a Python module
│
├── data <- Scripts to download or generate data
│ ├── make_dataset.py
│ ├── get_weights_1d.py <- Planing
│ ├── PCA_scaler.py <- PCA rotation
│ └── process_data.py <- Runs the preprocessing
│
├── models <- Scripts to train models and then use trained models to make
│ │ predictions
│ ├── HelperFunctions.py
│ ├── predict_model.py
│ ├── train_Adversarial.py
│ ├── train_base_nn.py
│ ├── train_BDT.py
│ ├── train_PCA_BDT.py
│ ├── train_PCA_nn.py
│ ├── train_planed_BDT.py
│ ├── train_planed_nn.py
│ └── train_uBoost.py
│
├── test_metrics <- Scripts to take histograms and compute metrics
│ ├── Distances.py
│ └── run_metrics.py
│
└── visualization <- Scripts to create exploratory and results oriented visualizations
Project based on the cookiecutter data science project template. #cookiecutterdatascience