AI Predicts Particle Collisions

This project leverages a TensorFlow model to predict particle collision outcomes from a dataset of collision images in .pkl format. The model’s predictions are output to a CSV file, which can then be analyzed using R scripts to generate scatterplots and histograms for performance evaluation.

The code is compatible with Python 3.9+ for model execution, while the R script requires R 4.2.1+ for data analysis. Key folders include:

• R Data Analysis: Contains the results in CSV format and an Rmarkdown file for data analysis.
• pycache: Implements the quantum neural network.
• Data: Stores the particle collision images.
• Images: Contains noteworthy experimental images.
• Main.ipynb: TensorFlow model and experiment code.

This project allows for effective particle collision prediction and detailed analysis of model accuracy.