Machine Learning AI Confirms 50 New Planets

by Ryan Whitwam

Spotting exoplanets isn’t as simple as pointing a telescope at the sky and picking out the planet-shaped things. The worlds orbiting distant stars are too dim and tiny for that, but we can detect them with the help of planet-hunting satellites like TESS and the dearly departed Kepler. These missions produce a lot of data that someone needs to evaluate, and researchers from the University of Warwick think they can speed it up with AI. To illustrate this, the team has developed a machine learning algorithm that just confirmed 50 exoplanets in observational data.

Astronomers have two methods at their disposal to detect exoplanets. There’s the radial velocity approach, which monitors stars for small counter-movements caused by the gravity of planets. A more sensitive technique, and the one employed by TESS and Kepler, relies on luminance variation in the host star. If a solar system’s plane is aligned correctly, its planets transit in front of the star from our perspective. By monitoring those dips in brightness, we can infer the presence of exoplanets with a high degree of certainty.

The problem with the transit method is that it produces a mountain of luminance data for stars, many of which will have no visible exoplanets. It takes a combination of computer analysis and human oversight to identify…