July 4, 2022

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Citizen scientists help discover more than 1,000 new asteroids

Citizen scientists help discover more than 1,000 new asteroids

Zoom / This mosaic consists of 16 different datasets from the NASA/ESA Hubble Space Telescope studied as part of the Asteroid Hunter citizen science project. Each of these data sets was assigned a color based on the chronological sequence of exposures. Blue tones represent the first exposure the asteroid was captured, and red tones represent the last exposure.

employment World Asteroid Day 2019, a group of research institutions have 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 The project identified more than 1,000 new asteroids; The discoveries could help scientists better understand the ring of celestial bodies that primarily float between Mars and Jupiter.

Asteroid Hunter is a collaborative effort between different groups, including the European Center for Science and Technology, the European Aerospace Center Science Data Center, the citizen science platform Zooniverse, and Google.

In 2019, researchers sent out a call for citizen scientists to collaborate on crowdsourcing efforts. Through the Zooniverse platform, 11,400 individuals from around the world mapped asteroid tracks in 37,000 composite images taken by the Hubble Space Telescope between 2002 and 2021.

“Hubble is an amazing mission, and it has produced a very rich database for astronomical observations over the years that we should build on,” Sandor Kroc, a postdoctoral researcher at the Max Planck Institute for Extraterrestrial Physics, told Ars. Pay attention to this long data period [that is] It’s starting to be available.” Kruk is involved with Asteroid Hunter.

Searching the sky

The results of the citizen’s scientific work were used to train a machine learning algorithm called AutoM, which was created by Google. When provided with sufficient data, the algorithm can now be used to quickly classify images.

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According to Kroc, there is a lot of variety in the asteroid tracks captured by Hubble. Usually, when you take a long-exposure image of an asteroid from Earth, the resulting path in the image is a line. But the combined motion of asteroids with Hubble’s motion produces curved trajectories. They are difficult to categorize using machine learning because they come in a wide variety of forms.

“That’s why I needed a sample of it that humans discovered,” Crooke said. “What took us a year to classify with citizen scientists — it only took about 10 hours with [algorithm]. But you need a training set.”

When worlds collide

The combined human-machine effort resulted in a data set containing 1,701 paths in 1,316 Hubble images. Participants also identified other objects in the images, such as galaxies and nebulae. They matched these tracks against those on the team Little Planet Center database, the largest database of asteroids, and found that 670 of them had been previously identified.

Croc said that the original items that the Asteroid Hunter had found seemed to be much weaker than the ones identified before, which meant they were even smaller in size. He pointed out that this work can be used to get a better idea of ​​the distribution of asteroid sizes in the asteroid belt, and that data can be used to understand more about their evolution and how asteroids are produced from fragmentation and collision within the belt.