The satellite, launched in 2018, is “exceptionally capable at detecting varying stars”, the researchers write in a preprint paper describing the initiative.
Researchers used machine learning to identify about 1.2 million potential eclipsing star pairs. Citizen scientists then validated a subset of about 60,000.
Overall, the citizen scientists identified 10,001 eclipsing binary pairs. Of those, 7936 were previously unknown.
Though machine learning can identify potential binary systems, researchers knew they would need to validate them - manually inspecting hundreds of thousands of images of eclipse-like events and weeding out actual binaries from images that tricked the algorithm.
“Thankfully,” the researchers write, “to the rescue come volunteers from all walks of life that boost the capacity of bandwidth-limited professional astronomers many-fold and help tackle the ever-increasing volume of publicly available astronomical data.”
The project is an example of how humans and computers can work together, Veselin Kostov, a research scientist at Nasa Goddard Space Flight Centre and the SETI Institute and lead author of the paper, said in a news release. “I can’t wait to search [the verified eclipsing binaries] for exoplanets!”
The citizen science project is still recruiting participants. Want to be part of the search for eclipsing binaries? Join the team at bit.ly/eclipsingbinaries.