Deep Learning Tutorials for Experimental Nuclear Physics

Deep Learning Tutorials for Experimental Nuclear Physics#

Welcome to the DNP 2025 Deep Learning Tutorials presented at APS-DNP 2025 Data Science Workshop sessions! This Jupyter Book provides comprehensive tutorials on applying deep learning techniques to experimental nuclear physics, specifically for the Forward Calorimeter (FCAL) at GlueX in Hall-D Jefferson Lab.

Overview#

These tutorials are designed for Nuclear and Paricle Physics and Researchers. We focus on two main applications of deep learning:

  1. CNN-based Classification: Using Convolutional Neural Networks to classify FCAL showers and distinguish between photons and splitoffs

  2. Generative AI: Building generative models to simulate FCAL photon showers based on their kinematics.

Tutorial Goals#

By the end of these tutorials, you will be able to:

  • Understand the physics behind FCAL showers and the importance of accurate classification

  • Prepare and preprocess FCAL data for deep learning applications

  • Build and train CNN models for binary classification (photons vs splitoffs)

  • Develop generative models (GANs, VAEs, or diffusion models) for FCAL shower simulation

  • Evaluate model performance using physics-informed metrics

  • Apply these techniques to your own nuclear physics research

Target Audience#

These tutorials are intended for:

  • Graduate students and postdocs in nuclear physics

  • Researchers working on calorimeter systems

  • Scientists interested in applying ML to experimental physics

  • Anyone wanting to learn about deep learning in the context of particle physics

Prerequisites#

  • Basic understanding of nuclear physics and particle detectors

  • Python programming experience

  • Familiarity with NumPy and basic data analysis

  • (Optional) Prior exposure to machine learning concepts

Dataset and models#

Dataset used in this tutorial: Hugging Face Dataset

The trained models are archived for easy access and reproducibility. You can explore and download them at: Hugging Face Model

Event Information#

DNP 2025 Tutorial Session
Date: October 17, 2025
Location: Chicago, IL
Workshop Sessions: 1WD, 2WD

For questions or feedback, please open an issue on our GitHub repository.

Authors Acknowledgements to GlueX

  • This tutorial is part of the AI4EIC collaboration’s effort to bring modern machine learning techniques to experimental nuclear physics.

  • We gratefully acknowledge the GlueX Collaboration for the software framework and the public release of the Monte Carlo simulation data used in this work GlueX acknowledgements.

Overview

CNN for FCAL

Generative AI for FCAL

Appendix

Contents