Python : Astronomical Data Analysis & Visualization

This course on astronomical  observational data visualization provides a comprehensive dive into the multidimensional world of spectroscopic image data cubes, the gold standard for modern observational astronomy. While a standard digital photograph captures two spatial dimensions (x, y), astronomical instruments like Integral Field Units (IFUs) capture a third dimension: wavelength. Participants will learn to navigate and manipulate FITS (Flexible Image Transport System) files, the universal data format in astronomy. We will deconstruct the "cube" architecture, where every spatial pixel (spaxel) contains a full spectrum, allowing us to map chemical compositions, temperatures, and velocities across celestial objects. Key Learning Objectives

• Data Structure: Master the coordinate systems (WCS) that link pixel indices to Right Ascension, Declination, and Frequency/Wavelength.

• Slicing & Dicing: Techniques for collapsing cubes into 2D intensity maps or extracting 1D spectra from specific regions of interest.

• Visualization & Analysis Tools: Hands-on experience with Python libraries (SpectralCube, Astropy) and software like DS9 or Glue to render 3D volumes.

• Kinematic Analysis: Using the Doppler shift within the cube to visualize the internal rotation of galaxies or the expansion of nebulae.

By the end of this course, you will transform raw, high-dimensional datasets into intuitive, scientifically robust visualizations that reveal the hidden physics of the universe.