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README.md

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 ---
-license: apache-2.0
-pipeline_tag: tabular-classification
----
+title: Hybrid Ensemble for Particle Track Reconstruction
+emoji: ⚛️
+colorFrom: purple
+colorTo: blue
+sdk: python
+sdk_version: 3.10
+pinned: false
+license: mit
+tags:
+  - high-energy-physics
+  - particle-track-reconstruction
+  - machine-learning
+  - ensemble-model
+  - cern
+---
+
+# Hybrid Ensemble Model for Particle Track Reconstruction and Classification
+
+## Model Overview
+
+This repository contains a **hybrid ensemble model** designed for **particle track reconstruction**, **particle type classification**, and **kinematic property estimation** in high-energy physics (HEP) experiments. The model processes raw detector hits from particle collisions (e.g., at the Large Hadron Collider) to reconstruct particle trajectories, classify particle types (e.g., electron, muon, pion), and predict kinematic properties such as momentum and energy. It integrates multiple machine learning techniques to achieve robust performance in dense collision environments.
+
+The model is built for researchers and HEP professionals, offering a scalable solution with potential for real-time implementation in large-scale experiments. It was evaluated on Monte Carlo-simulated detector events from [CERN's Open Data Portal](https://opendata.cern.ch/).
+
+## Model Architecture
+
+The model employs a multi-stage pipeline combining unsupervised and supervised learning:
+
+1. **Unsupervised Clustering**:
+   - Algorithms: HDBSCAN, K-Means, Gaussian Mixture Models (GMM), Agglomerative Clustering.
+   - Purpose: Groups raw detector hits into candidate particle tracks.
+
+2. **Feature Extraction**:
+   - Convolutional Neural Networks (CNNs) process spatial hit data to extract relevant features.
+
+3. **Trajectory Modeling**:
+   - Long Short-Term Memory (LSTM) networks model temporal dependencies in particle trajectories.
+
+4. **Regression**:
+   - Fully connected neural networks predict kinematic properties (momentum, energy, charge).
+
+## Dataset
+
+- **Source**: Monte Carlo-simulated detector events from CERN's Open Data Portal.
+- **Split**: 80% training, 20% testing.
+- **Description**: Simulated particle collision data, including raw detector hits with spatial and energy information.
+
+## Evaluation Metrics
+
+| Task            | Metric                  | Description                              |
+|-----------------|-------------------------|------------------------------------------|
+| Clustering      | Silhouette Score        | Measures accuracy of track formation     |
+| Classification  | F1 Score                | Evaluates particle type classification   |
+| Regression      | Mean Squared Error (MSE)| Assesses momentum and energy estimation  |
+| Overall         | Reconstruction Efficiency| Fraction of correctly identified tracks  |
+
+## Results
+
+- **Clustering**: Achieved robust track formation, with HDBSCAN producing 81 clusters and K-Means producing 8 clusters, as visualized in 2D and 3D plots.
+- **Track Reconstruction**: Successfully reconstructed particle trajectories, with 3D visualizations of tracks.
+- **Classification and Regression**: Demonstrated effective particle type classification and kinematic property estimation, though improvements are needed for extreme kinematic values.
+- **Training**: Stable training and validation losses over 8 epochs, as shown in loss curves.
+
+## Installation
+
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/your-repo/particle-track-reconstruction.git
+   cd particle-track-reconstruction
+
+
+Usage
+
+Prepare the Data:
+
+Ensure the CERN Open Data Portal dataset is accessible.
+Preprocess raw detector hits into the format expected by the model (e.g., numpy arrays of hit coordinates and energies).
+
+
+# Example
+To reconstruct tracks and classify particles:
+python main.py --data-path data/cern_events --mode reconstruct
+
+This will:
+
+Cluster raw detector hits using HDBSCAN and K-Means.
+Reconstruct tracks with CNNs and LSTMs.
+Classify particle types and predict momentum/energy.
+Save results (plots, metrics) to the results/ directory.
+
+# Limitations
+
+Extreme Kinematic Values: The model struggles to predict very high or low momentum/energy values accurately, requiring further optimization.
+Computational Cost: Dense collision environments may require GPU acceleration for real-time performance.
+Dataset Dependency: Performance is tied to the quality and diversity of the CERN Monte Carlo dataset.
+
+# Future Enhancements
+
+Optimize for real-time processing in HEP experiments.
+Improve regression performance for extreme kinematic values.
+Integrate additional clustering algorithms or deep learning architectures.
+Support custom datasets for broader applicability.
+
+
+For issues or contributions, contact the maintainers.
+```
+