ExoNET: an AI Saturdays Project

Source: Deep Learning on Medium

Exploring the Final Frontier

The Kepler space telescope was launched into heliocentric orbit in 2009 and was recently retired in 2018. Throughout its years of service, Kepler propelled a revolution in the detection of exoplanets – that is, planets from other stellar systems – and enabled the discovery of thousands of them by making use of the planetary transit method.

The planetary transit method? Let’s imagine a single star with one hypothetical planet in its orbit, and that the planet’s orbital plane intersects with our own point of view in the Solar System. Continuous imaging of the light coming from the star means that, at some point, when the planet passes in front of the star (called a transit), the intensity perceived by the telescope will decrease, as the planet partially eclipses the light from its star.

Example of planetary transit and its associated light curve.

This transit will happen again once the planet completes a full orbit around the star, which takes a fixed amount of time called the orbital period. If the telescope monitors the same star for enough time, it will perceive multiple transits evenly spaced in time. The repeated detection of these transits enables the discovery of new exoplanets, although confirmation often requires detection through a second method.

In the first part of its mission, lasting more than 4 years, Kepler recorded photometric data from over four million stars. This massive amount of data has been made available to the public through the NASA exoplanet archive.

Thankfully, the Kepler preprocessing pipeline automatically identifies candidate signals for planetary transits by looking for repeated events evenly spaced in time. However, it is far from infallible. There are many other types of events that can pass this first filter, such as measurement artifacts, eclipses from another star in a binary system, intrinsic fluctuations of the star’s brightness, or events in other star systems aligned with the candidate star from our point of view.

As a result, careful and often arduous vetting of these candidates is needed before committing resources into confirming a discovery. This is where we believe that machine learning can make a valuable contribution to the field.

This idea is not new. Shallue et.al used heavily processed photometric data and specifically light curves from confirmed planetary transits to train a Convolutional Neural Network (CNN). Can we do the same in four weeks?