A team of machine learning scientists and astronomers from Universities Space Research Association (USRA), the SETI Institute, and NASA discovered 69 new exoplanets using advanced machine learning techniques. The findings have been accepted for publication in the Astronomical Journal. This significant breakthrough was made possible by harnessing the power that artificial intelligence promises to expand our understanding of the universe and pave the way for future discoveries.
Exoplanets or planets outside our solar system have long captured the curiosity of scientists and public alike. Various approaches have been used for exoplanet discovery, including the transit method which led to the discovery of a majority of exoplanets. The Kepler and TESS mission efforts for example, were based on the transit method in which a target star is monitored for periodic dimming of its brightness known as a transiting event. However, not all detections resulting from transit events are exoplanets and could be due to different sources of false positives, such eclipsing binary stars.
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Traditionally, to ensure that the signal detected as an exoplanet is not due a false positive source, complementary observations are used to rule out those false positives. In contrast, however, statistical and machine learning techniques have advanced so that they rely on a new process called “validation” developed to discover new exoplanets. Instead of relying on the new observations to complement the transit method, the newly discovered 69 exoplanets are validated using the previously developed deep learning called ExoMiner and a concept called multiplicity. Astronomers strongly believe that multiplicity increases the probability and therefore the level of confidence that a new detected signal around a star that already has exoplanets is much higher than the ones that do not have one.
According to Dr. Hamed Valizadegan, a machine learning scientist at Universities Space Research Association and lead author of the paper, “By utilizing the information related to how many exoplanets have already been discovered for a given star, we could boost the ExoMiner’s confidence in ruling out false positives and validate 69 new exoplanets. The 69 newly discovered exoplanets vary widely in their characteristics, including size, orbital period and proximity to their host stars and can improve our understanding of the population of exoplanets in the universe. ”
The team has recently developed ExoMiner, a new deep neural network, that was used in 2021 to validate 301 new exoplanets. However, existing transit signal classifiers, including ExoMiner, do not use information regarding the configuration of a planetary system, e.g., number of existing confirmed planets or false positive signals. Utilizing the configuration of the system can help improve the confidence of a classifier to validate new exoplanets.
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The discovery of 69 new exoplanets using machine learning marks a pivotal milestone in exploratory research and propels us closer to answering fundamental questions about our place in the cosmos. As we continue to explore the vast depths of space, collaborations between astronomy and artificial intelligence are expected to redefine our understanding of the universe.
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