The Deep Underground Neutrino Experiment (DUNE) is integrating artificial intelligence into its detectors, data analysis, and operations. The Fermilab-led collaboration is using AI to reconstruct neutrino interactions, flag galactic supernovas in real time, and monitor detector health, building on machine learning work at the lab that goes back two decades.
Neutrinos are nearly massless particles that interact only through the weak force and gravity. Trillions pass through the human body every second, but a single interaction in a lifetime is rare. DUNE uses intense neutrino beams and massive liquid-argon time projection chambers to capture those interactions. The detectors record high-resolution images of particle tracks, which is where AI comes in.
The liquid-argon detectors produce near-photographic images of particle interactions. That detail helps scientists study neutrino properties, but it also makes reconstruction harder.
Teaching algorithms to spot neutrinos
"We are going to have very high resolution, almost photographic-quality images of these interactions," said Leigh Whitehead, DUNE AI/ML co-lead. "Which is great, but also brings challenges such as making reconstruction more difficult because we see such fine detail."
AI algorithms will identify when and where a neutrino interaction occurred, trace particle tracks back to the collision point, and estimate the neutrino's energy and direction. These tools run orders of magnitude faster than traditional reconstruction methods. The same pattern is showing up across scientific fields, which is why AI for Science & Research has become a core focus for researchers.
"We've had great success in implementing these tools in previous Fermilab experiments," said Jianming Bian, DUNE AI/ML co-lead. "And now DUNE is taking the leading role to further develop these algorithms and explore new AI methods that take advantage of its complex, large-scale data, making it a platform for AI/ML development."
Watching for supernovas
Beyond reconstructing routine neutrino events, DUNE is training AI triggers to spot rare phenomena. The most dramatic case: a supernova in our galaxy. When a massive star collapses, neutrinos escape the explosion hours before light does, so an AI algorithm constantly reads the detector looking for that signal.
"An AI algorithm will be constantly reading the detector, searching for interesting interactions," said Thomas Junk, senior scientist at Fermilab. The instant the algorithm suspects a supernova, "it will tell the system to record data from 10 seconds before and 100 seconds after the candidate signal to capture the full picture of neutrino interactions from this event."
The early alert gives astronomers time to point telescopes at the event. The neutrino data itself could reveal what object the star left behind - a neutron star or a black hole.
Running the detector with AI
DUNE's detectors contain thousands of components, and operators need to spot problems quickly. The collaboration is testing whether a large language model can scan maintenance logbooks and pull up documented fixes for current issues. Fermilab is coordinating with six other national labs on a similar tool for particle accelerators. The team is also looking at machine learning that could predict detector anomalies before they occur.
The Department of Energy's Genesis Mission is funding much of this AI work. "The Genesis Mission has strengthened the already existing collaboration across the national labs and universities on AI efforts," said Sowjanya Gollapinni, co-spokesperson for DUNE. "It is taking everything to the next level and will maximize our capabilities to accelerate DUNE."
The collaboration is also preparing for the petabytes of data DUNE will produce and training the next generation of researchers. "We have over a thousand people from around the globe collaborating on DUNE," said Whitehead. "We're training hundreds of students a year, which is a pretty impressive pipeline of people that should have all the tools to become leaders in the field." For professionals looking to build similar skills, AI Research Courses cover the kinds of model training and automation techniques being applied here.
Why this matters for science and research professionals
DUNE shows how AI is becoming standard infrastructure in large-scale physics - not just for analysis, but for real-time triggering and facility operations. The same pattern is appearing across scientific domains: AI speeds up routine reconstruction, flags rare events worth human attention, and helps operate complex instruments. The skills involved - training models on messy real-world data, building fast triggers, and using LLMs to query institutional knowledge - are directly transferable to other research settings.
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