Long-period planets orbit their star less frequently, meaning a longer period of time between dips in the light. Some planets are harder to find than others, too. Zink thinks partnerships with machine learning "could significantly improve our ability to detect exoplanets" in this kind of real-world, noisy data. However, satellites jiggle around in space and stars aren't perfect light bulbs, making transits sometimes tricky to detect. In data from telescopes like TESS, astronomers can spot faint dips in a star's light as a planet passes between it and the observatory, known as the transit method. Citizen science has the extra benefit of "sharing the euphoria of discovery with non-scientists, promoting science literacy and public trust in scientific research," Jon Zink, an astronomer at Caltech not affiliated with this new study, told .įinding exoplanets is tricky work - they're tiny and faint compared to the massive stars they orbit. People from across the world contributed by searching for and labeling exoplanet transits through the Planet Hunters TESS project on Zooniverse, an online platform for crowd-sourced science. "It's difficult to get labels on this scale without the help of citizen scientists," Nora Eisner, an astronomer at the Flatiron Institute in New York City and co-author on the study, told. After it's been trained, the algorithm can identify these features in new data it hasn't seen before.įor the algorithm to perform accurately, though, it needs a lot of this labeled training data. ![]() ![]() This computer algorithm looks at images or other information that humans have labeled correctly (a.k.a "training data"), and learns how to identify important features. The researchers used a typical machine-learning algorithm known as a convolutional neural network. Related: 10 amazing exoplanet discoveries
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