Notes on Astronomy Research Data Analysis Course

Overview

• Learn to analyze astronomical data using Python
• Practical hands-on approach to data analysis
• Course divided into four modules

Module 1: Basics of Python

• Introduction to Python basics essential for data visualization
• Importance of coding along to keep engaged
• Key Topics:
  • Python Basics
  • Variables and Constants
  • Data Types (String, Integer, Float)
  • Control Flow (Loops, If statements)
  • Functions

Key Points:

• Variables vs Constants: Variables can change, constants cannot.
• Data Types: Knowing the type of data (string, integer, float) is crucial for analysis.
• Control Flow: Essential for automating tasks, using loops and condition statements.
• Functions: Create reusable code blocks that help organize programs.

Module 2: Data Visualization

• Working with tabular data of real stars.
• Graph types created:
  • Bar Graphs
  • Box Plots
  • Line Plots
  • Pair Plots
  • HR diagram

Key Points:

• Data Set: Tabular data consisting of star properties (temperature, luminosity).
• Visualization Libraries: Utilized Matplotlib and Seaborn for graphing.
• Creating Histograms: Important for understanding pixel distribution.

Module 3: Handling Image Data

• Introduction to FITS files and image processing.
• Key Concepts:
  • Image Data: Understanding pixels, color channels, and how they combine to create images.
  • Image Querying: Using Sky View form to fetch images of Andromeda Galaxy.
  • Pixel Scaling Methods: Min-Max Scaling, Standardization, Log Normalization, and Square Root Scaling.

Key Points:

• FITS Files: Used for storing astronomical data.
• Image Processing Techniques: Applied various filters to enhance features in images.
• Extracting Features: Used corner detection and multi-scale basic features to identify characteristics within images.

Module 4: Advanced Feature Extraction

• Focus on denoising techniques using convolution with Gaussian kernels.
• Implementing filters to extract features from images.
• Key Techniques:
  • Measuring Filters: Highlighting linear features in images.
  • Setting Filters: Techniques like Sobel, Gaussian, etc.
  • Feature Extraction: Obtaining edges and corners for analysis.

Key Points:

• Convolution Operations: Used for applying filters to images.
• Feature Detection: Applied various methods for detecting features in astronomical images.
• Visualization Techniques: Enhance images for better clarity and detail.

Conclusion

• Successful completion of the course empowers students to perform data analysis and create visualizations.
• Skills developed can be applied in research work, internship reports, and future studies in astronomical data analysis.