A combination of citizen science and machine learning is a promising new technique for astronomers looking for exoplanets.
, an online platform for crowd-sourced science. 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 Space.com. they orbit. 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.
However, satellites jiggle around in space and stars aren't perfect light bulbs, making transits sometimes tricky to detect. Zink thinks partnerships with machine learning"could significantly improve our ability to detect exoplanets" in this kind of real-world, noisy data. Some planets are harder to find than others, too. Long-period planets orbit their star less frequently, meaning a longer period of time between dips in the light. TESS only studies each patch of sky for a month at a time, so for these planets may only capture one transit instead of several periodic changes.
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