MussabPro

MussabPro / Monte-Carlo-Simulation-of-Radioactive-Decay

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Monte Carlo particle decay simulator with detector inefficiencies, statistical validation, and interactive 2D/3D animations.

www.mussab.tech decay-simulator physics physics-simulation
26
0
100% credibility
Found Feb 25, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

An educational Python tool for simulating radioactive particle decay with random sampling, featuring graphs, animations, statistics, and realistic detector modeling.

How It Works

1
🔍 Discover the decay simulator

You stumble upon this fun tool while exploring particle physics experiments online.

2
💾 Get it on your computer

Download the simple files to start your own radioactive decay playground.

3
▶️ Run your first experiment

Pick a number of particles and watch them start decaying randomly.

4
📊 See the magic unfold

Beautiful graphs and curves appear, matching real science predictions perfectly!

5
🎬 Create cool animations

Make 2D grids, 3D clouds, or interactive views of particles fading away.

6
🔬 Add real-world twists

Mix in detector effects and channels to mimic actual lab experiments.

🏆 Become a decay expert

With plots, stats, and videos in hand, you now grasp radioactive decay like a pro physicist!

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Star Growth

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AI-Generated Review

What is Monte-Carlo-Simulation-of-Radioactive-Decay?

This GitHub Monte Carlo simulation Python tool models radioactive particle decay, generating exponential decay times for thousands of particles while factoring in multi-channel branching ratios and realistic detector effects like efficiency losses, time cuts, and resolution smearing. Run it via simple CLI examples to get histograms, decay curves, statistical tests (chi-squared, bootstrap CIs), and publication-ready plots. It also spits out interactive 2D grid animations, rotating 3D particle clouds, and a real-time Pygame sim for watching decays unfold.

Why is it gaining traction?

Among GitHub Monte Carlo simulations, it stands out with built-in detector modeling and rigorous stats validation that match theory within 5%, plus eye-catching 2D/3D visuals that make stochastic processes tangible—no extra setup needed. Devs dig the quick-start API for custom params (particle count, decay constant) and example scripts that auto-save results to folders. It's a polished demo for GitHub Monte Carlo Python projects, blending physics accuracy with smooth animations.

Who should use this?

Particle physics students applying to programs like KEK's Belle II summer school, flavor physicists prototyping B-meson decays, or educators needing interactive decay demos for quantum mechanics classes. Data scientists exploring Monte Carlo sampling GitHub tools for stats-heavy sims will find the validation pipeline handy.

Verdict

Grab it if you're in physics ed or research prototyping—docs are thorough, 53 tests pass, and outputs are pro-grade despite 19 stars and 1.0% credibility score signaling early maturity. Skip for production; it's more portfolio piece than battle-tested lib.

(198 words)

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