A team of researchers used citizen scientists and machine learning to discover about 1,000 new asteroids in archived Hubble images.
“The amount of data in astronomy archives increases exponentially and we wanted to make use of this amazing data.”The astronomers released the results of their project in a new paper titled “
The streaks go to the heart of the problem: computers struggle to detect them. That’s where the Zooniverse platform and citizen scientists come in. The work of the citizens who took part trained a machine-learning algorithm to search the rest of the images quickly and accurately. The algorithm exists in the Google Cloud, and once it was trained, it contributed another 900 detections for a total of 2487 potential asteroid trails in the Hubble data.
These asteroids have escaped detection because they’re fainter and likely much smaller than most asteroids detected from the ground. This paper is the first one released as part of the Hubble Asteroid Hunter project. In subsequent papers, the authors will use the curved shape of the asteroid trails to determine their orbits and their distances.
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