The combined power of citizen science and machine learning have led to the discovery of more than 1,000 new asteroids in archival images taken by the Hubble Space Telescope.

Asteroid trail in Crab Nebula image by Hubble
The faint trail of 2001 SE101, a previously known main-belt asteroid, appears as a curved streak that crosses this image of the Crab Nebula from bottom left to top right, near the nebula’s center.
ESA / Hubble & NASA / M. Thévenot (@AstroMelina)

More than 1,000 previously unknown asteroids have been found on old Hubble photos — not by professional astronomers, but by citizen scientists and a machine-learning module. “This work is a great example of how serendipitous science can take place in large datasets,” comments asteroid researcher Bill Bottke (Southwest Research Institute), who was not involved in the study.

Over the past three decades, hundreds of thousands of deep-sky images have ended up in the Hubble archives. Occasionally, tiny asteroids passed through the field of view during the exposure, leaving a faint trail on the photo. Such asteroid trails are usually curved, because Hubble itself is also moving in its orbit.

In 1998 Robin Evans and Karl Stapelfeldt (both at Jet Propulsion Laboratory) unearthed dozens of “photobombing” asteroids by visually inspecting Hubble images. But since then, no one has carried out another dedicated search. “Evans and I have been too busy with other projects to keep up with the flow of new Hubble images to search,” says Stapelfeldt.

Sandor Kruk (Max Planck Institute of Extraterrestrial Physics, Germany) and Pablo García Martín (Autonomous University of Madrid, Spain) took a different approach. Building on Kruk’s experience with GalaxyZoo, a citizen-science program originally developed to help astronomers classify galaxies, they developed a similar program called Hubble Asteroid Hunter, and invoked the help of thousands of volunteers from all over the world.

The number of known asteroids in the solar system is steadily increasing. This animation maps all known asteroids discovered between 1999 and 2018. The newly discovered asteroids in Hubble images add to this ongoing census.

Mining for Asteroids

Identifying asteroids on Hubble images is difficult. Most of the trails are exceedingly faint. Moreover, viewers must distinguish them from other elongated artifacts, such as satellite trails, cosmic ray tracks (caused by charged particles slamming into the detector), and faint gravitational lens arcs.

Kruk and his colleagues selected 37,323 Hubble images obtained over the past 20 years with Hubble’s Advanced Camera for Surveys and Wide Field Camera 3. They cut the images into four quadrants, each of which was inspected by 10 volunteers. Their task was to identify every possible track, mark the start and end points, and if possible, provide a classification.

By the end of the program, 11,482 volunteers had searched through 144,559 quadrants. Between them, they ended up with a whopping 1.78 million individual classifications (155 per person on average). By critically comparing and combining all these data, the team ended up with 1,488 potential asteroid trails – on average just one for every 100 image quadrants.

Meanwhile, Kruk’s team fed the reported classifications into a machine-learning module, developed in collaboration with Google scientists. After sufficient training, the algorithm rediscovered about two-thirds of the trails that the citizen scientists had found. In addition, it found almost 1,000 new trails in images that no one had looked at before.

“The availability and enthusiasm of citizen scientists were pivotal for the development of the automated algorithm,” says Stapelfeldt, who is “happy to see this work go forward.” Kruk agrees. “In principle, we could now let the algorithm search for asteroid trails in each and every new Hubble image,” he says. “This would never have been possible without the efforts of citizen scientists.”

But even with 2,487 trails on hand, the team wasn’t done yet. As they describe in a paper to appear in Astronomy & Astrophysics (preprint available here), Kruk and two other team members visually inspected each of them, weeding out remaining cosmic ray events that had not properly been recognized as well as false or ambiguous identifications. In the end, a total of 1,701 valid asteroid trails remained.

Next, the team checked the results against the existing database of small solar system objects hosted by the Minor Planet Center of the International Astronomical Union. In 670 cases, they identified the trail with a known object. For example, the relatively bright asteroid 2001 SE101 passed in front of the Crab Nebula on a Hubble image obtained in 2005. That left 1,031 unidentified trails, most of them very faint: 23rd-magnitude on average.

“To me, the most exciting thing is that this kind of science turns out to be possible at all with the Hubble data, despite the fact that Hubble was never designed to do this,” says Kruk.

Bottke is equally enthusiastic. “This paper shows there are many ways to maximize the science we can get from all telescopic observations,” he says, “and that entrepreneurial amateur astronomers can produce some really neat results.”

What to Do with 1,000 New Asteroids

In future work, Kruk and his colleagues will analyze the trail shapes of the new asteroids, which will enable them to derive distances. They can then translate the observed brightness of an object into an estimate of its physical size. Eventually, this will provide more precise information on the size distribution of the smallest objects in the solar system.

“We know the main [asteroid] belt is collisionally evolved, with collisions being the primary geologic process affecting asteroids,” says Bottke. “By probing the number of small bodies that exist, we get critical input for computer models that allow us to estimate both the strengths of asteroids and how long they are likely to survive prior to being disrupted. This tells us about asteroid evolution in general.”

René Laureijs (European Space Agency, ESA) looks forward to future cooperation with citizen scientists in ESA’s Euclid mission, for which he is the project scientist. Euclid will map two-thirds of the entire sky at the same resolution as Hubble to study dark matter and dark energy in the universe. “With Euclid, the main focus will be on the morphology of galaxies,” Laureijs says, “but we also expect to find at least 150,000 small solar system objects in our data.”


Image of RMP


February 6, 2022 at 11:27 am

Professional ground-based observatories are greatly concerned about the mega-satellite constellations like Starlink now being launched by the thousands in LEO, will drastically effect their research and search in particular, transient objects and phenomenon. Some 'amateurs' claim they should just remove satellite trails from the data, as many amateurs do to their images. The 1000 newly found asteroids of described in this article, found even in the narrow FOV of Hubble, is a 'shining' example of what could be lost if professional and even citizen scientists work with 'doctored' data that remove 'transient trails of pixels' caused by 10's of thousands of trails every night, caused by the mega-constellations of satellites.

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February 6, 2022 at 2:39 pm

I and other citizen scientists found quite a few satellites in the Hubble images as part of this project as well. Not sure if the team are going to follow these trails up in addition to the asteroid trails.

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February 6, 2022 at 3:03 pm

It might be interesting to collect the statistics of the satellites to know how frequently and bright they are for the Hubble's FOV, but otherwise I don't know what would be gained. The statistics might provide a very relevant data-set for comparison and prediction, of the many 10s of thousands of Starlink and other mega-constellations that might be filling the skies of Earth based observatory telescopes soon.

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February 7, 2022 at 6:54 am

Hi @RMP, this is exactly what we are planning to do in follow-up study. We already have a paper, currently in the refereeing process, presenting the statistics of artificial satellites in Hubble, using the classifications of @John-Murrell and other citizen scientists. So stay tuned!

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February 13, 2022 at 12:21 am

Hmmm... it occurs to me that these photobombing satellites (and yes, even asteroids) are registered precisely because photos are taken with a single long-duration exposure. But if we switched to the increasingly-common technique of stacking many short-duration images, then a solution suddenly appears. Seems like all you'd have to do is tell the computer stacking algorithm to ignore any photons that appear on only 1 frame (or 2, or 3, whatever small number works out best). Then the satellite image will "magically" disappear. The astronomer could look at the image both ways, and choose the "doctored" one only if the "standard" one had a satellite track.I imagine there may be complications, that need some extra image processing "smarts," but I don't see any major roadblocks to this approach, as long as the software actually supports the "1-frame transient removal" task in the first place.

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March 30, 2022 at 6:35 am

That is what is done for cosmic ray hits. I am not sure how the Hubble exposure system works but one of the problems with multiple short images is that the read out noise adds which may result in a worse signal to noise ratio. I From what I have seen the Hubble pipeline software tries to remove continuous trails but if the satellite is close the broad trail is not eliminated completely and you get the 'ploughed field' effect.

Also asteroid images that look similar though are often curved are of interest to some as in this project.

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