A Highly Efficient AI Orion Scans 400,000 Images to Find Over 27,000 New Asteroids

Sunday - May 12 2024, 19:36 UTC - 8 months ago

The Asteroid Institute, in collaboration with Google Cloud, has discovered over 27,000 new asteroids using AI and machine learning algorithms. The majority of these asteroids are in the main asteroid belt, but over 100 are near-Earth asteroids. The team plans to use the data to train an AI model, making the asteroid discovery process even faster. The upcoming Vera C. Rubin Observatory in Chile will also aid in the search for Earth-crossing asteroids.

There are well over a million asteroids in the solar system. Most don't cross paths with Earth, but some do and there's a risk one of these will collide with our planet. Taking a census of nearby space rocks, then, is prudent. As conventional wisdom would have it, we'll need lots of telescopes, time, and teams of astronomers to find them.

But maybe not, according to the B612 Foundation's Asteroid Institute.

In tandem with Google Cloud, the Asteroid Institute recently announced they've spotted 27,500 new asteroids—more than all discoveries worldwide last year—without requiring a single new observation. Instead, over a period of just a few weeks, the team used new software to scour 1.7 billion points of light in some 400,000 images taken over seven years and archived by the National Optical-Infrared Astronomy Research Laboratory (NOIRLab).

To discover new asteroids, astronomers usually need multiple images over several nights (or more) to find moving objects and calculate their orbits. This means they have to make new observations with asteroid discovery in mind. There is also, however, a trove of existing one-time observations made for other purposes, and these are likely packed with photobombing asteroids. But identifying them is difficult and computationally intensive.

Working with the University of Washington, the Asteroid Institute team developed an algorithm, Tracklet-less Heliocentric Orbit Recovery, or THOR, to scan archived images recorded at different times or even by different telescopes. The tool can tell if moving points of light recorded in separate images are the same object. Many of these will be asteroids.

Running THOR on Google Cloud, the team scoured the NOIRLab data and found plenty. Most of the new asteroids are in the main asteroid belt, but more than 100 are near-Earth asteroids. Though the team classified their findings as 'high-confidence,' these near-Earth asteroids have not yet been confirmed. They'll submit their findings to the Minor Planet Center, and ESA and NASA will then verify orbits and assess risk. (The team says they have no reason to believe any pose a risk to Earth.) While the new software could speed up the pace of discovery, the process still requires volunteers and scientists to manually review the algorithm's finds. The team plans to use the raw data from the recent run including human review to train an AI model. The hope is that some or all of the manual review process can be automated, making the process even faster.

In the future, the algorithm will go to work on data from the Vera C. Rubin Observatory, a telescope in Chile's Atacama desert. The telescope, set to begin operations next year, will make twice nightly observations of the sky with asteroid detection in mind. THOR may be able to make discoveries with only one nightly run, freeing the telescope up for other work.

All this is in service of the plan to discover as many Earth-crossing asteroids as possible.

According to NASA, we've found over 1.3 million asteroids, 35,000 of which are near-Earth asteroids. Of these, over 90 percent of these bi .