Astrophysics and Python

Knowing the stellar parameters can provide a very good understanding of the star under observation. However, given only the effective temperature and log surface gravity for the star in MaStar catalog file the remaining stellar parameters (mass, radius, and luminosity) were determined using equations and assumptions. Black body curve for the corresponding effective temperature is also shown at the end for a selected few stars.

The relationship between basic stellar parameters e.g, star’s mass, radius, and luminosity, given its surface gravity and temperature is derived.

Summary post for the analysis of the SDSS/MaStar catalog file.

The Sloan Digital Sky Survey (SDSS) is one of the biggest digital surveys available in the astronomy/astrophysics community. Starting from 2000, the SDSS has completed four phases of the survey. The data has been used in various catalogs and sub-surveys as well. The SDSS data has also been used to create on the the most detailed maps of the universe. In this post, we will see how we can use the SDSS/MaNGA Stellar Library (MaStar) and what inferences we can make from the data.

A general look at the stars. Going from early star exploration to the extended research and problems that the diverse classification of stars pose in their complete understanding. As an added bonus, how scientists torture the dying stars.

Conversion of celestial coordinates using Python

This post discusses the formulae for interconversion of celestial coordinate systems. For example, altitude to zenith, hour angle to RA, DMS/HMS to DD.

Celestial coordinates like Horizontal, Equatorial, Ecliptic and Galactic coordinate systems along with their components are discussed.

Spherical coordinate system, a brief overview and some python codes

This post discusses the two most basic coordinate systems, (1) number line, (2) Cartesian coordinate system, and their Python code.