Galaxy Classification with Neural Networks in the Sloan Digital Sky Survey

🚀 Using Neural Networks to Classify Galaxies 🌌

I led the development and analysis of a project that applied machine learning techniques to classify galaxy shapes using imaging data from the Sloan Digital Sky Survey (SDSS). This work explored how convolutional neural networks (CNNs) can handle the rapidly growing datasets in astronomy, enabling scalable and efficient classification of galaxy morphologies.

Key Outcomes:

• Built and trained a CNN on over 300,000 galaxy images.
• Demonstrated the potential of neural networks for large-scale image classification tasks, even with limited training data.
• Identified avenues for improvement, including expanding datasets, optimizing model configurations, and parallelizing computations for scalability.

Why It Matters for Industry: This project highlights my experience in developing machine learning pipelines, optimizing performance, and handling large datasets—skills directly applicable to data science, machine learning, and research-driven innovation.

Let’s connect to discuss how these techniques can create value in your organization! 🌟