Cosmic Morphology

Hubble Beholds a Big, Beautiful Blue Galaxy
NGC 2336, captured by the NASA/ESA Hubble Space Telescope.

Overview

This project focuses on building a scalable data infrastructure to store and analyze real cosmic data retrieved from the NASA API. The primary goal is to develop a robust multiclass classification model capable of identifying various celestial phenomena, such as galaxies, nebulae, and stars.

Objectives

• Scalable Infraestructure: Create a system for retrieving, storing and processing image data from APIs.
• Classify Cosmic Morphology: Multiclass classification model to identify 6 classes; asteroid, comet, galaxy, nebula, planet, star.
• Enhance System Performance: Further model fine-tuning,

📂 Methods

• 1-Data-Collection: Scripts for data extraction from NASA API, fetching cosmic images.
• 2-EDA: Notebooks and scripts for Exploratory Data Analysis (EDA), including class imbalance visualizations.
• 3-Train-Val-Test-Split: Code for splitting data into training, validation, and test sets for model training.
• 4-Machine-Learning: Contains:
  • Baseline Model: Initial ResNet50-based training and evaluation.
  • Fine-Tuning: Transfer learning with additional layers and parameters.
  • Evaluation: Metrics visualizations and model performance analysis.

Results

Conclusions

• The fine-tuned model showed modest improvements in precision and recall for certain classes, notably "nebula" and "galaxy".
• Class imbalances and noisy data remain significant challenges affecting overall performance.

Next Steps

• Clean and refine image data to remove mislabeled or irrelevant samples.
• Try out other transfer learning models and pre-trained weights.
• Expand the dataset by incorporating the SpaceNet dataset and evaluate the impact of synthetic data augmentation on model generalization.

Bibliography

• Lintott, C. J. et al. (2008). Galaxy Zoo: Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey. Monthly Notices of the Royal Astronomical Society, 389(3), 1179–1189.
• Willett, K. W. et al. (2013). Galaxy Zoo 2: Detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey. Monthly Notices of the Royal Astronomical Society, 435(3), 2835–2860.
• Chollet, F. (n.d.). Image Classification from Scratch. Keras. Retrieved from https://keras.io/examples/vision/image_classification_from_scratch/
• Chollet, F. (n.d.). Keras Metrics. Keras. Retrieved from https://keras.io/api/metrics/
• Nicholas Renotte. (n.d.). Build a Deep CNN Image Classifier with ANY Images. YouTube. Retrieved from https://www.youtube.com*