This project explores the application of machine learning techniques for astronomical pattern recognition, specifically for classifying celestial objects into three categories:
- Star
- Galaxy
- Quasi-Stellar Object (QSO)
Using observational data from astronomical surveys, the project applies machine learning models to analyze feature patterns and accurately classify celestial objects.
The project demonstrates how data science and machine learning can be applied to astrophysical datasets to support automated astronomical object classification.
The objectives of this project are:
- Analyze astronomical datasets containing observational features
- Identify patterns that differentiate stars, galaxies, and quasars
- Develop machine learning models for object classification
- Evaluate model performance using classification metrics
The dataset contains several observational features commonly used in astronomical classification, such as:
- Spectral characteristics
- Photometric measurements
- Object brightness
- Redshift indicators
- Other astrophysical attributes
These features are used as input variables for machine learning classification models.
- Python
- Scikit-learn
- Pandas
- NumPy
- Matplotlib
- Jupyter Notebook
- Data Exploration and Visualization
- Data Cleaning and Preprocessing
- Feature Selection and Analysis
- Dataset Splitting (Training and Testing)
- Model Training
- Model Evaluation
- Result Visualization
Examples of algorithms used:
- Logistic Regression
- Decision Tree
- Random Forest
- K-Nearest Neighbors (depending on implementation)
The performance of the models is evaluated using:
- Accuracy
- Precision
- Recall
- Confusion Matrix
- Classification Report
The trained models demonstrate the ability to classify stellar objects with strong accuracy by identifying patterns within astronomical observational data.
This project highlights how machine learning can assist astronomers in processing large-scale astronomical datasets.
- Apply deep learning for astronomical image classification
- Integrate additional astrophysical datasets
- Improve feature engineering techniques
- Deploy the model for automated astronomical classification systems
Hans Alexander
Informatics Engineering Student
Interests: Data Science, Machine Learning, Physics, Astronomy