Planet Hunting with Python

The Kepler telescope was first launched in March 2009 and spent 4 years collecting data from more than 150,000 stars. 4717 exoplanet candidates have been discovered from various solar systems. Our aim is to design an algorithm using Python to find exoplanets using raw data of light intensity from distant stars.

Concept

When a planet transits the star it orbits, the observed intensity of light from the star drops by a small amount. We can use this data to not just find but also calculate the orbits of exoplanets in distant solar systems.
The process of discovering these planets is not as complicated as some may expect. If the Kepler telescope is continuously focused on one region in space, you gain information regarding the transit of these planets in relation to their stars. The telescope detects the luminosity of these stars. When a planet passes in front of its star, this luminosity will decrease, as the intensity of light shining onto the lens of the telescope decreases. Since the planets orbit their star repeatedly, we can expect to see a periodic dimming of the stars, thus giving us information regarding the orbital period and the size of these exoplanets.
For geometric reasons, most planetary orbits around distant stars do not line up perfectly with the telescope's line of sight. For a randomly oriented orbit, the probability of observing the transit is about 1%, so for this reason we want the telescope to monitor as many stars as possible(150,000 for the duration of the mission), leaving us with the discovery of 4717 exoplanets candidates, of which 2303 have been confirmed to be genuine exoplanets.

Download the PDF of Planet Hunting project