August 5, 2026
New AI system successfully schedules observations on a major national telescope
| The NSF Víctor M. Blanco 4-meter Telescope at the U.S. National Science Foundation Cerro Tololo Inter-American Observatory (CTIO), a Program of NSF NOIRLab in Chile, is observing. An artificial intelligence system has for the first time successfully planned and adapted observations on a national facility, demonstrating a new way to make the most of scarce observing time. Trained on 13 years of historical data from the DOE-funded Dark Energy Survey (DES), the system generated an observing schedule for the Blanco Telescope and revised it in real time as weather and other conditions changed. (Credit: CTIO/NOIRLab/NSF/AURA/G. Damke) |
Every night, astronomers must carefully assess changing weather, the intensity of moonlight and shifting atmospheric conditions before deciding where to point a telescope. It’s a constant balancing act designed to squeeze as much science as possible from every precious hour beneath dark skies.
Now, University of Chicago, Fermilab, and Northwestern University scientists have developed a new artificial intelligence (AI) tool that automatically determines where a telescope should point.
After developing the tool in the National Science Foundation (NSF)-Simons Foundation AI Institute for the Sky (SkAI, pronounced “sky”), the scientists successfully used the AI system to schedule observations with the 570-megapixel Dark Energy Camera (DECam), which was funded by the DOE, built at Fermilab, and mounted on the NSF Víctor M. Blanco 4-meter Telescope at Cerro Tololo Inter-American Observatory (CTIO) in Chile. Not only did the system generate an observing plan, but it also adapted that plan in real time as environmental conditions changed. By automating routine scheduling decisions, the innovation will help telescopes collect the best possible data.
“This is an important milestone toward more autonomous observatories,” said project co-lead Alex Drlica-Wagner, UChicago professor of astronomy and astrophysics and Fermilab scientist. “One of the main achievements is that we set up all the infrastructure needed to deploy this self-driving telescope on a national observatory. Currently, I would say its performance is comparable to a human’s ability. As the next step, we plan to teach the computer to do a better job than a human.”
“It is exciting to see ideas from AI and reinforcement learning brought to telescope scheduling, where every decision must balance changing conditions and scarce observing time,” said Aravindan Vijayaraghavan, associate professor of computer science at Northwestern’s McCormick School of Engineering, who co-led the project with Drlica-Wagner. “Developing intelligent scheduling systems for astronomical surveys also raises fascinating new machine learning problems, and we are excited to continue exploring them through this project.”
On-sky deployment at the Blanco telescope was performed by Paul Chichura, SkAI postdoctoral associate and associate fellow at UChicago’s Kavli Institute for Cosmological Physics; UChicago physics graduate student Rachel Hur; and Guillermo Damke, an associate scientist at NSF’s NOIRLab.
Choosing where to point a telescope isn’t just about finding an interesting object to observe. It’s also about making the most of every minute of valuable observing time. Sometimes, astronomers wait months for a chance to use a major telescope. A poorly positioned telescope could return less sharp images or washed-out images flooded by moonlight, making faint or distant objects even more difficult to detect. And the opportunity to redo the failed observation might be months away.
“If the data quality is not sufficient for your science goals, you cannot re-run the lost night,” said Hur. “That raises the bar considerably: every decision has to be good enough to justify running on a national facility. Being able to test that reliability on Blanco—outside simulations, on real nights with real consequences—is a major milestone.”
To make the observing process more efficient, the project team combined expertise in large astronomical surveys with cutting-edge machine learning. Rather than programming AI with rules astronomers have developed over decades, the researchers let a deep-learning system learn on its own. They trained a deep-learning model on historical observations from the DOE-funded Dark Energy Survey, which scans the night sky with a giant camera mounted on the Blanco Telescope. This synthesis of astronomy and AI was uniquely enabled by the SkAI Institute.
“We trained the model on years of historical observations by showing it where the telescope was pointing at one moment and asking it to predict the next observation,” Drlica-Wagner said. “Then we compared its prediction to what astronomers actually did and asked it to correct its mistakes. After repeating this process many times, it learned how to schedule observations without being explicitly taught how the brightness of the moon, the atmospheric conditions, or the many other factors affect the quality of astronomical observations.”
This past spring and summer, the intelligent scheduling system completed two successful observing runs on the Blanco Telescope—one of the world’s most productive astronomical facilities. For this initial deployment, the goal was to get the AI to perform about as well as human schedulers. The team’s next goal is to teach the AI not just to mimic human decision-making but improve upon it. By exploring observing strategies humans might never consider, AI eventually could make telescopes even more efficient.
“We’ve built our software infrastructure to be flexible, allowing us to swap in even more sophisticated models capable of juggling a wider range of scientific goals and observing styles,” said Chichura. “We hope that this will help speed up the iterative design process that can help push the field of astronomy forward.”
As next-generation telescopes including the NSF–DOE Vera C. Rubin Observatory begin producing unprecedented amounts of astronomical data, intelligent scheduling systems could help companion telescopes respond more efficiently and maximize the scientific value of every observation run.
“If we can automate this technical operational task so it requires less human effort, then astronomers can have more time to think about more scientifically interesting problems and focus on discovery,” Drlica-Wagner said.
This work is one expression of the multidisciplinary UChicago AI Initiative, which aims to bring artificial intelligence to bear across research and education—from adaptive instruments like this telescope to new tools and methods throughout the sciences and beyond.
About SkAI
Led by Northwestern University, SkAI is a National AI Research Institute jointly funded by the NSF and the Simons Foundation. SkAI brings together researchers in astronomy, AI and related fields to develop trustworthy AI tools that accelerate scientific discovery, advance cutting-edge astronomical surveys and instruments, and train the next generation of interdisciplinary scientists.
-Adapted from an article published by Northwestern University.