Stellar Classification using Deep Learning techniques

A supervised classification task to predict star type based on Surface Temperature (in K), Luminosity (measured with respect to that of Sun), Radius (measured with respect to that of Sun), Absolute Magnitude (Visual), Star Color, & Spectral Class.

The types of stars predicted at the end of training the model are:

1. Brown Dwarf (0)
2. Red Dwarf (1)
3. White Dwarf (2)
4. Main Sequence (3)
5. Supergiant (4)
6. Hypergiant (5)

Dataset Source

https://www.kaggle.com/deepu1109/star-dataset

Data Collection and Preparation techniques:

The dataset is created based on several equations in astrophysics. They are given below:

1. Stefan-Boltzmann's law of Black body radiation (To find the luminosity of a star)
2. Wienn's Displacement law (for finding surface temperature of a star using wavelength)
3. Absolute magnitude relation
4. Radius of a star using parallax.
5. The missing data were manually calculated using those equations of astrophysics given above.

Inspiration

Interest in Astronomy & Stellar Evolution