https://github.com/pngo1997/astrophysical-objects-classification

Project applies machine learning techniques to classify astrophysical objects using observational data from the Large Synoptic Survey Telescope (LSST).
https://github.com/pngo1997/astrophysical-objects-classification

adaptive-boosting-algorithm classification down-sampling gradient-boosting machine-learning neural-network python random-forest supervised-learning time-series

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Project applies machine learning techniques to classify astrophysical objects using observational data from the Large Synoptic Survey Telescope (LSST).

• Host: GitHub
• URL: https://github.com/pngo1997/astrophysical-objects-classification
• Owner: pngo1997
• Created: 2025-01-31T16:50:22.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-31T17:38:05.000Z (about 1 year ago)
• Last Synced: 2025-01-31T17:41:07.737Z (about 1 year ago)
• Topics: adaptive-boosting-algorithm, classification, down-sampling, gradient-boosting, machine-learning, neural-network, python, random-forest, supervised-learning, time-series
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🌌 Astrophysical Objects Classification  

## 📜 Overview  

This project applies **machine learning (ML) techniques** to classify **astrophysical objects** using observational data from the **Large Synoptic Survey Telescope (LSST)**. By leveraging **time-series and static numerical features**, we aim to develop an accurate model for classifying celestial objects, such as **comets and asteroids**, based on their spatial positions, light curves, and motion.

## 🎯 Problem Explanation  

The **LSST dataset** consists of **astronomical time-series data** collected through **optical sky scanning**. The telescope tracks the **brightness and motion** of celestial objects to support cosmic mapping and asteroid threat assessments.

### **Challenges in Classification**  

1. **Complex Feature Representation**:  

   - The dataset contains both **static** (e.g., position, redshift) and **time-series** (e.g., brightness over time) attributes.  

   - Integrating these features for classification is **non-trivial**.  

2. **Imbalanced Classes**:  

   - Certain astrophysical objects are overrepresented, leading to **biased model predictions**.  

3. **Selection of the Best ML Model**:  

   - Decision trees, boosting methods, and neural networks have **different strengths** for time-series and tabular data.  

   - We evaluate **multiple models** to determine the best approach.  

📌 **Dataset**: LSST Photometric Time Series (PLAsTiCC Competition)  

🔢 **Observations**: 4,925 total (2,459 train | 2,466 test)  

📊 **Classes**: 16 astrophysical object types  

## 📊 Data Preprocessing & Feature Engineering  

### **1️⃣ Dataset Overview**  

The dataset includes **two types of data**:  

| **Feature Type** | **Description** |

|----------------|--------------------------------|

| **Static Features** | Spatial attributes (RA, DEC, Galactic coordinates) |

| **Time-Series Features** | Brightness variations over time |

### **2️⃣ Handling Class Imbalance**  

- **Applied class weighting** to ensure fair representation.  

- **Downsampling techniques** adjusted sample distribution.  

### **3️⃣ Feature Engineering**  

- **Rolling time-window transformations** on flux values.  

- **Feature selection** using correlation analysis.  

- **Normalization & scaling** applied for neural network compatibility.  

## 🔍 Exploratory Data Analysis (EDA)  

### **1️⃣ Meta Data (Static Features)**  

- **Right Ascension (RA) vs. Galactic Longitude (GL)** shows an **inverse parabolic trend**.  

- **High variance in brightness motion data** across objects.  

📊 **EDA Visualization**:  

- **Feature correlation heatmaps**  

- **Class distribution histograms**  

- **Light curve plots of six sample objects**  

## 🤖 Machine Learning Models  

### **1️⃣ Random Forest (RF) 🌲**  

✔️ **Strengths**:  

- Handles **high-dimensional data** well.  

- Provides **high accuracy & interpretability**.  

📊 **Results**:  

- **Train Accuracy**: **97%** | **Log Loss**: **1.05**  

- **Downsampled Accuracy**: **91%** | **Log Loss**: **3.17**  

### **2️⃣ Gradient Boosting (GB) 🚀**  

✔️ **Strengths**:  

- **Reduces bias** via sequential boosting.  

- More effective in handling **complex relationships**.  

📊 **Results**:  

- **Train Accuracy**: **68%** | **Log Loss**: **11.64**  

- **Downsampled Accuracy**: **65%** | **Log Loss**: **12.53**  

### **3️⃣ Adaptive Boosting (AB) 🏋️**  

✔️ **Strengths**:  

- Assigns **weights to observations**, improving classification of hard-to-predict samples.  

📊 **Results**:  

- **Train Accuracy**: **46%** | **Log Loss**: **19.54**  

- **Downsampled Accuracy**: **23%** | **Log Loss**: **7.78**  

### **4️⃣ Neural Networks (NNs) 🧠**  

✔️ **Strengths**:  

- Captures **complex temporal dependencies** in time-series data.  

- Uses **LSTM layers** for time-dependent pattern recognition.  

📊 **Results**:  

- **Train Accuracy**: **46%**  

- **Weighted Accuracy**: **59%**  

- **Log Loss**: **9.6**  

## 🏆 Best Performing Model  

📌 **Random Forest (RF) is the most effective model**:  

- **Highest accuracy (97%)** on full train data.  

- **Performs well with both static & time-series features**.  

- **Faster training time** compared to Boosting & Neural Networks.  

🚀 **Key Observations**:  

- **Feature selection improved model performance** by **removing redundant features**.  

- **Downsampling maintained class distribution** while improving computational efficiency.  

- **Neural Networks struggled with time complexity and performance tuning**.

  

## 📢 Key Findings & Recommendations  

✅ **Key Insights**:  

- **Light curve patterns are highly predictive** of object classification.  

- **Feature selection significantly improves classification accuracy**.  

- **Class imbalance correction is essential** for fair model evaluation.  

🔧 **Recommended Future Work**:  

- 📊 **Test alternative deep learning architectures** (CNNs for feature extraction).  

- ⚡ **Optimize feature engineering** for time-series processing.  

- 🔬 **Use SMOTE for class balancing** instead of downsampling.  

## 🚀 Technologies Used  

🛠 **ML Frameworks**:  

- **Python (Scikit-learn, TensorFlow, Keras)**  

- **Pandas, NumPy** for preprocessing  

- **Matplotlib, Seaborn** for visualization  

📡 **Dataset**: [LSST Photometric Time Series Data (PLAsTiCC)](https://timeseriesclassification.com/description.php?Dataset=LSST)