Citizen scientists help discover over 1,000 new asteroids


Enlarge / This mosaic consists of 16 different NASA/ESA Hubble Space Telescope datasets that were studied as part of the Asteroid Hunter citizen science project. Each of these datasets was assigned a color based on the temporal sequence of exposures. The blue tones represent the first exposure in which the asteroid was captured, and the red tones represent the last.

On International Asteroid Day in 2019, a group of research institutions launched a program that could have a profound impact on our knowledge of tiny bodies. Using citizen science to train a machine learning algorithm, the Hubble Asteroid Hunter Project has identified over 1,000 new asteroids; the findings could help scientists better understand the ring of celestial bodies that mostly float between Mars and Jupiter.

Asteroid Hunter is a collaborative effort between various groups, including the European Center for Science and Technology, the European Center for Space Astronomy’s Science Data Center, citizen science platform Zooniverse, and Google.

In 2019, the researchers issued a call for citizen scientists to collaborate in the crowdsourcing effort. Using the Zooniverse platform, 11,400 members of the public around the world identified asteroid trails in 37,000 composite images taken by the Hubble Space Telescope between 2002 and 2021. Citizen scientists pored over the images for a year and identified more than 1,000 contrails.

“Hubble is an incredible mission, and it has produced a very rich database of astronomical observations over the years that we should take advantage of,” Sandor Kruk, postdoctoral fellow at the Max Planck Institute for Extraterrestrial Physics, told Ars. “We should pay more attention to this long period of data [that is] are starting to become available. Kruk is involved in Asteroid Hunter.

looking for the sky

The results of the citizen science work were used to train a machine learning algorithm called AutoM, which was created by Google. When it has enough data, the algorithm can now be used to quickly classify images.

According to Kruk, there is great diversity in the asteroid trails captured by Hubble. Normally, when taking a long exposure image of an asteroid from the ground, the resulting trail in the image is a line. But the combined motion of asteroids with Hubble’s motion produces curved trails. These are harder to classify using machine learning because they come in a wide variety of forms.

“That’s why you needed a sample of them detected by humans,” Kruk said. “What took us a year to classify with the citizen scientists – it only took about 10 hours with the [algorithm]. But you need the training set.

When worlds collide

The combined human-machine effort resulted in a dataset containing 1,701 tracks in 1,316 Hubble images. Participants also identified other objects in the images, such as galaxies and nebulae. They compared these tracks to those in the Minor Planet Center team’s database, the world’s largest database of asteroids, and found that 670 of them had already been identified.

The originals found by Asteroid Hunter appeared much weaker than those previously identified, meaning they were smaller in size, Kruk said. He noted that this work could be used to get a better idea of ​​the size distribution of asteroids in the asteroid belt, and that the data could be used to better understand their evolution and how asteroids are produced at from fragmentation and collision within the belt.


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