Machine Learning Finds 301 More Planets in Kepler Data

Using a new deep neural network called ExoMiner, scientists find another 301 exoplanets in the existing Kepler and K2 mission data.

IMAGE: Over 4,500 planets have been found around other stars, but scientists expect that our galaxy contains millions of planets. There are multiple methods for detecting these small, faint bodies around much larger, bright stars. CREDIT: NASA/JPL-Caltech

Not to be outdone by TESS, the Kepler mission continues to expand our exoplanet catalog by leaps and bounds. In a new paper in The Astrophysical Journal, scientists used a deep neural network called ExoMiner and NASA’s Supercomputer Pleiades to comb through Kepler and K2 mission data in an attempt to speed up the process of finding and confirming exoplanets.

Kepler and TESS both use the same method of detecting exoplanets — the transit method — where we measure the amount of light coming from a star and see if there are any dips in that light that could indicate the presence of an orbiting planet. There are a few issues with this method, and one of them is that false positives can appear due to another star or starspots or even dust. And looking at each star with a human eye is incredibly time-consuming. I know. I’ve done it.

And that’s where machine learning comes in. We can train a neural network to differentiate between an exoplanet and those false positives using data we have already processed. And with over 4,500 exoplanets in the Kepler data already validated, there is plenty of…