I'll admit to using AI for things it was probably never designed for. When my van threw a fault I couldn't immediately figure out—one that seems notorious for trips back and forth to a repair shop—I ended up in a long (but cheaper) back-and-forth with an AI system, working through the symptoms. The same happened with a run of gutters and with stud framing in my house. It isn't magic, and perhaps more often than I'd like, it's wrong, but as a way of thinking through a problem with something that's read more manuals than I ever will, it has earned its place in the toolbox. So I read this next story with some sympathy and interest.

AI has now been turned to the sky because astronomers have a scheduling problem, and it's a harder one than mine. Time on a major telescope is rationed. You might wait months for a handful of hours, and those nights arrive with conditions you can't control. The moon might be too bright for a faint target. The seeing might be poor. Clouds might roll across the part of the sky you'd planned to work in. Many of these can be planned around, but it's a tricky, moving beast, and every hour is a judgment call about which of your targets is worth the conditions you've actually got. Getting it wrong means soft images, washed-out data and a wait until the schedule comes around again.

Alex Drlica-Wagner of Fermilab and the University of Chicago, and Aravindan Vijayaraghavan at Northwestern, have built something to make that decision. Working through the SkAI institute, they trained a deep learning model on years of observations from the Dark Energy Survey. They didn't encode the rules astronomers use. Instead, they showed the model where the telescope was pointing at a given moment, asked it to predict what happened next, then compared its guess with what the humans actually did and made it correct itself. Repeat that a few million times, and the model works out how moonlight and the atmosphere shape a good decision without ever being told that they do.

Having now learned how "astronomers do it," they scheduled two real observing campaigns this spring and summer on the Blanco 4-meter at Cerro Tololo in Chile. The system was able to drive the 570-megapixel Dark Energy Camera, producing the plan and then adapting it live as conditions changed.

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The honest assessment from the team is that it currently performs about as well as a human. That sounds modest until you consider that matching an experienced astronomer was the entire goal of a first deployment. The next objective is to exceed them by trying strategies no human would think to try.

There's a practical reason this matters now: With the Vera Rubin Observatory about to start producing more data than anything before it, other telescopes will need to react quickly to what it finds. Scheduling by hand doesn't scale to that, but an AI tool can react and adapt at speeds no human can match.

Drlica-Wagner puts the point better than I could: "... automate the operational work, and astronomers get their time back for the interesting part." Which is roughly what I want from my AI system, although my ambitions stop at the van for now.

Who's behind this story?

Lisa Lock

BA art history, MA material culture. Former museum editor, paramedic, and transplant coordinator. Editing for Science X since 2021. Full profile →

Andrew Zinin

Master's in physics with research experience. Long-time science news enthusiast. Plays key role in Science X's editorial success. Full profile →

Citation: The telescope that points itself (2026, August 11) retrieved 11 August 2026 from https://phys.org/news/2026-08-telescope.html

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