Tag: python

Cartesian coordinate system, a brief overview and some python codes

This post goes through a bit of NumPy history, NPY file, and will walk through a npy reader GUI application in python.

This post discusses the usage of Kepler’s third law in case of our solar system. How it can be used to determine the semi-major axis given a planet’s time period and vice versa.

This post is the end result for the MaStar data processing. The parameters obtained through the given effective temperature, surface gravity, and the PSF magnitudes like mass, radius, luminosity, distance, flux etcetera are all discussed and visualized.

This post gives an overview of how telescopes gather data about celestial objects, how filter works, the SDSS ugriz filters, their conversion to UBVRI, color indices, how they can be determined and what do they have to do with temperature of stars.

This post discusses the methods of determining the distance to stars using the SDSS MaStar data magnitudes.

For g, r, and I filters for the PSF magnitudes provided the magnitudes were first converted to physical flux densities. The flux densities were then converted to flux, then the fluxes to radius using the flux-radius-luminosity relationship. After determining the distance, the apparent magnitudes were converted to absolute magnitudes of the star

Stellar magnitudes play an important role in classifying the stars. This post discusses both the apparent and absolute magnitudes of the star and the relationships from which these parameters can be obtained.

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.

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.