🚀 ML-Enhanced Particle Beam Steering & Anomaly Detection

This project is a high-precision computational tool designed to simulate particle dynamics in accelerators and utilize Machine Learning for beam stability monitoring. Developed as a demonstration of applying Computer Engineering principles to "Big Science" infrastructures.

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🔬 Project Overview

Modern particle accelerators (like those at CERN, SOLEIL, or MAX IV) produce massive amounts of data and require sub-nanosecond control precision. This project addresses two critical challenges:

1. High-Fidelity Simulation: Modeling charged particle trajectories in a magnetic field using advanced numerical integration.
2. Autonomous Diagnostics: Using AI to detect beam anomalies, ensuring the safety and efficiency of large-scale scientific facilities.

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🛠️ Technical Architecture

1. Physics Engine (src/particle_simulator.py)

• Algorithm: 4th Order Runge-Kutta (RK4) Method.
• Physics: Solves the Lorentz Force Equation: $F = q(E + v \times B)$.
• Why RK4? Unlike simple Euler integration, RK4 minimizes cumulative error, making the simulation scientifically viable for long-term tracking.

2. Data Engineering (src/data_generator.py)

• Generates thousands of simulation scenarios by varying magnetic field intensities ($B_z$).
• Creates a structured dataset of beam outcomes for training diagnostic models.

3. Machine Learning Layer (src/anomaly_detector_ml.py)

• Model: Isolation Forest (Unsupervised Learning).
• Function: Learns the "stable" beam patterns and flags any deviations (hardware failures, field noise, or beam loss) as anomalies.

4. Interactive Dashboard (visualization/interactive_dashboard.py)

• Built with Streamlit and Plotly.
• Allows researchers to adjust magnetic parameters via a UI and see real-time 3D trajectory updates.

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🚀 Getting Started

Prerequisites

• Python 3.8+
• pip (Python package manager)

Installation

1. Clone the repository:

   git clone [https://github.com/yourusername/ML-Enhanced-Particle-Simulator.git](https://github.com/yourusername/ML-Enhanced-Particle-Simulator.git)
   cd ML-Enhanced-Particle-Simulator
   

Install dependencies:

• Bash
• pip install -r requirements.txt

Running the Project

• Generate Data: python src/data_generator.py

• Train AI: python src/anomaly_detector_ml.py

• Launch Dashboard: streamlit run visualization/interactive_dashboard.py

🛠️ Technologies Used

Language: Python

Math/Physics: NumPy, SciPy

Machine Learning: Scikit-learn, Joblib

Data/UI: Pandas, Plotly, Streamlit

Author

Ahmet Meriç Yıldırım