There are lots of articles that describe what principal component analysis (PCA) is, when should you use PCA, how to use PCA, and even a thorough walkthrough on the mathematical process behind PCA. What we are going to do in this series (yes, it would be more than one article) is what PCA is widely used for: find the most representative variables that describe the data. In this case, it is the condition of the interstellar medium.

We are going to make an attempt to reproduce the work of Ensor et al. 2017 (Paper I hereafter) that explores the line-of-sight…

An astronomer-in-the-making with interest in data of any fields, astronomical observation, and stellar spectra. Loves coffee and classical music.