🔬 Higgs Boson Classification: Machine Learning in Particle Physics

🌟 Project Overview

An advanced binary classification implementation using Deep Neural Networks to identify Higgs Boson events in high-energy particle physics experimental data.

🧲 The Higgs Boson: A Fundamental Particle

Physical Significance

The Higgs Boson is a fundamental particle discovered in 2012 at CERN's Large Hadron Collider (LHC), representing a monumental breakthrough in particle physics. It is the quantum excitation of the Higgs field, a fundamental field of crucial importance in the Standard Model of particle physics.

The Higgs Mechanism

• Explains how fundamental particles acquire mass
• Validates the Standard Model of particle physics
• Discovered through extremely complex and rare decay processes
• Predicted theoretically in 1964, experimentally confirmed in 2012

Decay Characteristics

The Higgs Boson is extremely unstable, decaying almost immediately into other particles. The dataset we're using captures these complex decay signatures, which are challenging to distinguish from background noise.

📊 Dataset Details: CERN Higgs Boson Challenge

Data Origin

• Source: CERN Large Hadron Collider (LHC)
• Collected during high-energy particle collision experiments
• Part of a machine learning challenge to classify Higgs Boson events

Dataset Characteristics

• 30 features describing particle physics events
• Binary classification: Signal (Higgs Boson) vs Background
• Highly preprocessed and normalized experimental data
• Represents complex interactions at subatomic scales

Feature Types

• Kinematic properties of detected particles
• Energy measurements
• Spatial and momentum information
• Derived physics-based calculations

🧠 Technical Overview

Objective

Classify events into two categories:

• Signal (Higgs Boson present)
• Background (Experimental noise)

🚀 Key Features

• Deep Neural Network with Dropout
• Robust data preprocessing
• Regularization techniques
• Comprehensive evaluation metrics

🔬 Neural Network Architecture

Network Design

Input Layer (30 features)
↓
Fully Connected Layer (128 neurons)
↓ ReLU Activation
↓ Dropout (20%)
↓
Fully Connected Layer (64 neurons)
↓ ReLU Activation
↓ Dropout (20%)
↓
Output Layer (Sigmoid)

Hyperparameters

• Layers: 3 (2 hidden + output)
• Neurons: 128 → 64 → 1
• Activation Function: ReLU
• Dropout: 20%
• Optimizer: Adam
• Learning Rate: 0.001
• Weight Decay: 1e-5

🚀 Dependencies

Libraries Used

• torch: Deep Learning
• sklearn: Preprocessing and metrics
• pandas: Data manipulation
• numpy: Numerical computations
• matplotlib, seaborn: Visualization
• shap: Model interpretability
• tensorboard: Training monitoring

💻 Installation & Execution

Prerequisites

• Python 3.8+
• pip
• CUDA (optional, for GPU)

Installation

pip install torch sklearn pandas numpy matplotlib seaborn shap tensorboard

📈 Methodology

Processing Steps

1. Data Loading
2. Preprocessing
   • Removal of irrelevant columns
   • Label mapping
3. Train/Test Split
4. Normalization (StandardScaler)
5. Neural Network Training
6. Performance Evaluation

🧮 Evaluation Metrics

Calculated Metrics

• Accuracy
• Precision
• Recall
• F1-Score
• AUC-ROC
• Confusion Matrix

Results Interpretation

• Model performance in classifying Higgs events
• Analysis of false positives/negatives

🔍 Exploratory Analysis

Visualizations

• Feature histograms
• Correlation matrix
• Distribution boxplots

Techniques

• Correlation heatmap
• Feature distribution analysis
• Pattern and outlier identification

🤖 Model Interpretability

SHAP (SHapley Additive exPlanations)

• Explanation of individual predictions
• Feature importance
• Impact of each variable on decision

🔬 Complexity Analysis

Space

• O(n): Linear complexity with number of features
• Memory: Dependent on dataset size

Time

• O(m * k): m = epochs, k = batch size
• Training: ~50 epochs

🦾 Possible Extensions

• Experiment with deeper architectures
• Ensemble techniques
• Increase dataset size
• Implement early stopping
• Explore alternative architectures

📊 Typical Results

Metrics

• Accuracy: ~85-90%
• AUC-ROC: ~0.85-0.90
• Precision: ~0.80-0.85
• Recall: ~0.80-0.85

📝 Contributions

Contributions are welcome! For significant changes, please open an issue first.

📋 License

MIT

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