Kalman-Filter Charged Particle Tracker

This Git repository serves as the culmination of my MSc project, focusing on the implementation of the Kalman-Filter technique from scratch. The project centers around a 2D toy model charged particle tracker, specifically designed to work with a transverse magnetic field.

Repository Structure

• data/

  • This folder contains the datasets used for the project. These datasets are used as input for the Kalman-Filter implementations.

• makeTracks/

  • This directory includes scripts and utilities for generating and manipulating track data for charged particles.

• utils/

  • Utility scripts and helper functions that support the main Kalman-Filter implementations.

• CKFwithChargedParticles.py

  • Implementation of the Combinatorial Kalman-Filter algorithm with charged particles.

• KFwithChargedParticles.py

  • Basic Kalman-Filter implementation specifically for tracking charged particles in a transverse magnetic field.

• KFwithNeutralParticles.py

  • Implementation of the Kalman-Filter algorithm for tracking neutral particles, included for comparison purposes.

Project Description

The primary goal of this project is to implement the Kalman-Filter technique from scratch for tracking charged particles in a 2D environment with a transverse magnetic field. The project includes multiple implementations of the Kalman-Filter algorithm to handle different types of particles and scenarios.

Key Features

• Kalman-Filter Implementations:

  • The repository includes three main Python scripts that implement the Kalman-Filter technique for different particle scenarios: CKF with charged particles (CKFwithChargedParticles.py), basic charged particles (KFwithChargedParticles.py), and neutral particles (KFwithNeutralParticles.py).

• Utility Functions:

  • The utils directory provides additional support functions that aid in data processing, visualization, and algorithm implementation.