Using a new deep neural network called ExoMiner, a team of scientists from Universities Space Research Association (USRA), NASA, and other institutions recently discovered 301 new exoplanets to the total exoplanet tally. The throng of planets is the latest to join the 4,569 already validated planets orbiting a multitude of distant stars. The results are published in the Astrophysical Journal.
Deep neural networks are machine learning methods that automatically learn a task when provided with enough data. ExoMiner is a new deep neural network that leverages NASA’s Supercomputer, Pleiades, and can distinguish real exoplanets from different types of imposters, or “false positives.” Its design is inspired by various tests and properties human experts use to confirm new exoplanets. And it learns by using past confirmed exoplanets and false positive cases.
ExoMiner supplements the work of people who are pros at combing through data and deciphering what is and isn’t a planet. For missions like Kepler spacecraft with thousands of stars in its field of view, each holding the possibility to host multiple potential exoplanets, it’s a hugely time-consuming task to pore over massive datasets. ExoMiner solves this dilemma.
“When ExoMiner says something is a planet, you can be sure it’s a planet,” said Hamed Valizadegan, ExoMiner project lead and machine learning manager with the Universities Space Research Association at Ames. He is also the lead author of the paper published in the Astrophysical Journal. “ExoMiner is highly accurate and in some ways more reliable than both existing machine classifiers and the human experts it’s meant to emulate because of the biases that come with human labeling.”
Jon Jenkins, exoplanet scientist at NASA’s Ames added, “These 301 discoveries help us better understand planets and solar systems beyond our own, and what makes ours so unique.”
Confirmed and Validated Exoplanets
What is the difference between a confirmed and validated exoplanet? A planet is “confirmed,” when different observation techniques reveal features that can only be explained by a planet. A planet is “validated” using statistics – meaning how likely or unlikely it is to be a planet based on the data.
The Astrophysical Journal paper explains that ExoMiner validated the 301 planets using data from the remaining set of possible planets – or candidates – in the Kepler Archive. All 301 machine-validated planets were originally detected by the Kepler Science Operations Center pipeline and promoted to planet candidate status by the Kepler Science Office. But until ExoMiner, no one was able to validate them as planets.
USRA’s Miguel Saragoca Martinho, the main engineer behind implementing ExoMiner, said, “The modular design of ExoMiner allows us to explain why it says something is planet or false positive. That is a peace of mind for domain experts when using a black-box machine classifier such as ExoMiner.”
The research also demonstrates how ExoMiner is more precise and consistent in ruling out false positives and better able to reveal the genuine signatures of planets orbiting their parent stars – all while giving scientists the ability to see in detail what led ExoMiner to its conclusion.
As the search for more exoplanets continues – with missions using transit photometry such as NASA’s Transiting Exoplanet Survey Satellite, or TESS, and the European Space Agency’s upcoming PLAnetary Transits and Oscillations of stars, or PLATO, mission – ExoMiner will have more opportunities to prove it’s up to the task.
“Now that we’ve trained ExoMiner using Kepler data, with a little fine-tuning, we can transfer that learning to other missions, including TESS, which we’re currently working on,” said Valizadegan. “There’s room to grow.”