High-speed Particle Collision Simulation with 3D Visualization and Data Analysis
This project simulates high-energy particle collisions inspired by experiments at CERN’s Large Hadron Collider (LHC). Using Python and Jupyter Notebook, it applies object-oriented programming principles to model particle dynamics, collisions, decays, and transformations. The simulation features real-time 3D visualization, interactive controls via a GUI, comprehensive data logging, and performance optimization for large-scale runs.
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Define a Particle class with attributes: position, velocity, mass, energy, and type (e.g., proton, meson, lepton).
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Implement methods for updating position, computing kinetic energy, decay detection, and displaying particle information.
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Build a Collider class to detect collisions based on proximity and apply momentum conservation.
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Add stochastic transformations (Monte Carlo) for particle splitting and fusion events.
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Create a Simulation class to initialize particles with random states.
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Control the simulation loop with adjustable time steps.
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Implement start, pause, and reset controls.
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Develop a GUI using tkinter for user parameter inputs (particle count, speed, etc.).
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Develop a Visualizer class utilizing Matplotlib’s 3D toolkit.
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Animate particle trajectories, collision events, decays, and transformations in real time.
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Color-code different particle types and overlay a heatmap of collision frequencies.
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Implement a DataLogger class to record collision events, decays, and fusion outcomes into CSV/JSON.
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Write analysis scripts to generate summary reports, charts, and graphs using Pandas and Matplotlib.
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Integrate simple machine learning models to predict resultant particle types from collision data.
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Profile code with cProfile to identify bottlenecks.
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Refactor key loops and algorithms for speed and memory efficiency.
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Employ Python’s multiprocessing library to parallelize large simulations.
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Write unit tests for all modules to ensure correctness.