One of the biggest challenges in astronomy is also the most obvious: space is big, and it takes a long time to look at it all. This is why artificial intelligence has been such a boon to this science. It turns out that the same machine vision tools developed for tasks like guiding self-driving cars are also perfect for sorting through vast amounts of astronomical data. So, astronomers announced this month that they’d used AI to find 6,000 new craters on the Moon.

Now, this isn’t that significant in itself. The Moon is estimated to have hundreds of thousands of craters, mostly caused by impacts with asteroids and meteors. This is because of a few factors. First, because the Moon has no atmosphere, these objects have a free path down to the surface (unlike on Earth where air friction slows them down and reduces them in size). Second, because there’s no weather on the Moon, these marks aren’t smoothed away by erosion. The end result is the crater-faced satellite we all know.

But using AI to find these craters is important, as it demonstrates another way machine learning can automate a labor-intensive task. The less time astronomers have to spend flicking through pictures of the Moon, labeling craters by hand, the more they have to focus on other, more challenging research. Plus, the more we know about the Moon’s craters, the better we can theorize about the history and formation of our Solar System.

The tool used for this particular research is what’s known as a convolutional neural network, or CNN. This is a common technique that’s particularly good at sorting through visual data. As the researchers who conducted the work explain in an unpublished paper, they trained their network using a data set of craters previously identified by humans. Once the program had learned what craters looked like, it was turned loose on a new section of the Moon’s surface (roughly one-third of its total surface area). There, it found 6,000 new craters.

As the scientists who conducted the work, from the universities of Toronto, Penn State, and Arizona State, write in their paper, the system was consistent and, most importantly, fast. “Once trained, our CNN greatly increases the speed of crater identification, taking minutes to generate predictions for tens of thousands of Lunar DEMs,” they write. (A DEM is a digital elevation map, and it is the standard image-type used to find and classify craters.) “This is, of course, all done passively, freeing the scientist to do other tasks.”

This system wasn’t perfect, and when tested against a human crater-spotter, it only found 92 of the same feature. But this work shows AI is an extremely capable tool that can speed up basic astronomic research. To date, similar methods have been used for spotting gravitational lenses, discovering new exoplanets, identifying pulsar stars, and classifying galaxies. Space may be big, but humans now have computers to help them sift through the cosmos.