JetPointNet

Overview

JetPointNet adapts the PointNet architecture to enhance energy measurement precision in particle physics. This project processes CERN Root files into machine learning-compatible formats, enabling sophisticated analysis of collision events from the ATLAS detector.

Key Components

• Data Preprocessing Pipeline: Converts Root files to Awkward Arrays, then to NumPy format
• Model Architecture: Customized PointNet with physics-inspired modifications
• Training & Evaluation: Specialized scripts for model training and performance assessment

Project Structure

JetPointNet/
├── python_scripts/
│ └── data_processing/
│ ├── jets/
│ │ ├── jets_awk_to_npz.py
│ │ ├── jets_root_to_awk.py
│ │ ├── preprocessing_header.py
│ │ ├── track_metadata.py
│ │ └── util_functs.py
│ └── jets_training/
│ ├── models/
│ ├── jets_test.ipynb
│ ├── jets_train.py
│ └── jets_tune.py
└── README.md

Requirements

• Python 3.8+
• TensorFlow 2.5.0+
• CUDA 11.1+
• Awkward Array 1.6.0+
• Uproot 4.0.0+
• NumPy 1.20.0+

Setup

1. Acquire Athena Events Dataset
2. Clone the repository:
3. Install dependencies:
4. Configure CUDA environment (if using GPU)

Usage

1. Preprocess data step 1 (Root to Awkward Array): run python_scripts/data_processing/jets/jets_root_to_awk.py

2. Preprocess data step 2 (Awkward Array to Numpy): run python_scripts/data_processing/jets/jets_awk_to_npz.py

3. Train model: run python_scripts/data_processing/jets_training/jets_train.py

4. Evaluate model: Run python_scripts/data_processing/jets_training/jets_test.ipynb

Results

Preliminary tests demonstrate promising qualitative performance of around 73% in attributing significant energy deposits within the detector to specific source particles.

Future Work

• Implement hyperparameter optimization
• Explore attention mechanisms for improved performance
• Scale the model to handle larger datasets

Contributing

We welcome contributions to the JetPointNet project, feel free to fork away!

Acknowledgements

We gratefully acknowledge the support of TRIUMF, CERN and the ATLAS group in providing the data and infrastructure necessary for this research.

Note: Please note that the onboarding documentation is a bit out of date (as of 2024/04/26)