This project contains the following files and folders:

QCDex1: Contains 500 trained models.

QCDex2: Contains 500 trained models.

Quantum_Chromodynamics_err.ipynb: Training script, including sampling code, neural network framework, training, and testing code.

data: Training data files and 1000 pre-sampled samples.

plot_all.ipynb: Plotting script for visualizing results. This script can be run directly for reproducibility. Alternatively, you can train your own models using the Q.ipynb script.

File Descriptions

File Descriptions

QCDex1 and QCDex2 These folders contain 500 trained models each. Due to file size limitations, only the prediction data has been uploaded. The models themselves are not included in the repository.

Quantum_Chromodynamics_err.ipynb This Jupyter notebook contains the training script. It includes:

Sampling Code: Code to sample data for training.

Neural Network Framework: The architecture of the neural network used for training.

Training and Testing Code: Code to train the model and evaluate its performance.

data.rar This compressed file contains the training data files and 1000 pre-sampled samples. To use this data, you need to extract the contents of the .rar file.

plot_all.ipynb This Jupyter notebook contains the plotting script. It is designed to visualize the results of the trained models. You can run this script directly to reproduce the plots. Alternatively, you can train your own models using the Q.ipynb script and then use this plotting script to visualize your results.

Getting Started

Extract Data:

Download and extract the contents of data.rar to access the training data and pre-sampled samples.

Training Models:

Open Quantum_Chromodynamics_err.ipynb in Jupyter Notebook.

Follow the instructions in the notebook to train your own models.

Visualizing Results:

Open plot_all.ipynb in Jupyter Notebook.

Run the notebook to generate plots based on the trained models.

Notes

Model Files: The trained models are not included in the repository due to file size limitations. You can train your own models using the Q.ipynb script.

Reproducibility: The plot_all.ipynb script is designed to be run directly for reproducibility. However, you can also train your own models and use this script to visualize your results.

Dependencies Ensure you have the following dependencies installed:

Jupyter Notebook

PyTorch

Matplotlib (for plotting)

mplhep

Other dependencies as specified in code

Contact

For any questions or issues, please open an issue in the repository or contact the project maintainer.

Thank you for using this project! We hope it helps you in your research or development work.