One of the world’s most ambitious neutrino experiments, the Deep Underground Neutrino Experiment (DUNE), is being built to catch some of the universe’s most elusive particles, but finding those fleeting signals may soon involve artificial intelligence.
As the massive detector takes shape a mile underground in South Dakota, researchers are developing AI systems to sort through huge amounts of data, identify neutrino interactions, spot signs of a stellar explosion and even detect problems inside the detector.
This could fundamentally change how scientists run a particle-physics experiment at DUNE’s scale.
For instance, instead of waiting for conventional analysis to work through enormous amounts of data, AI could identify important events in near real time and direct researchers toward the signals most worth investigating.
“These approaches are going to really accelerate DUNE in terms of the commissioning process and the sensitivity of the experiment to reach our full discovery potential,” Sowjanya Gollapinni, a representative from DUNE, said.
Can AI read ghost-particle tracks?
Neutrinos are notoriously difficult to study. Trillions pass through our bodies every second, yet they interact so rarely that detecting them requires an enormous detector and an intense neutrino beam.
Fermilab will generate a powerful neutrino beam that will travel to a detector at the Sanford Underground Research Facility in South Dakota, where huge chambers filled with liquid argon will record the tiny traces left when neutrinos collide with argon atoms.
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See AllKeeping so much argon in the right state is itself a huge engineering challenge. Each of DUNE’s two planned far-detector cryostats will eventually house about 17,000 tons of liquid argon, kept at roughly −300°F. The massive cryogenic containers must keep the argon cold enough for the detector to work.
However, the problem does not end when a neutrino is detected. Every interaction can produce several particles, each leaving a trail through the argon.
Scientists must work backward through those tracks to determine where the collision occurred, what particles were created, and what the original neutrino’s energy and direction were.
Fermilab researchers have already spent years testing whether machine learning can make this process faster. The MicroBooNE experiment was among the first high-energy physics experiments to use deep neural networks to analyze images from a liquid-argon detector. DUNE is now taking that approach much further.
“We are going to have very high resolution, almost photographic-quality images of these interactions. Which is great, but also brings challenges such as making reconstruction more difficult because we see such fine detail.” ,” Leigh Whitehead, an AI expert at DUNE, said.
An AI lookout for exploding stars
DUNE’s AI will also be searching for something far rarer—a supernova in our galaxy.
When a massive star collapses and explodes, neutrinos escape from its interior and can reach Earth before the burst of light becomes visible. This gives scientists an unusual opportunity to study the violent processes occurring inside a dying star.
DUNE’s giant liquid-argon detector is being built in part to capture neutrinos from astrophysical events such as nearby supernovae.
An AI trigger will continuously monitor DUNE’s detector for the telltale pattern of a supernova neutrino burst. If it finds a promising signal, the system is designed to preserve data from 10 seconds before to 100 seconds after the candidate event.
This could give astronomers an early warning, allowing telescopes to turn toward the exploding star while its light is still arriving.
The neutrinos themselves could reveal what the explosion leaves behind — potentially a neutron star or a black hole. This makes DUNE part particle detector, part cosmic early-warning system.
AI could become DUNE’s technician
There is another problem hidden inside DUNE’s enormous scale, which is keeping the detector running. Thousands of components must operate together underground, and operators need to respond quickly when something goes wrong.
Researchers are therefore investigating whether a large language model could search through records of previously solved problems and point operators toward relevant fixes.
Machine learning could eventually go a step further by recognizing patterns that indicate a detector anomaly before an actual failure occurs.
This approach, therefore, reflects a broader push to use AI throughout particle-physics experiments, where increasingly complex facilities are producing more data than conventional methods can easily handle.
Building an AI-powered physics experiment
DUNE will eventually produce petabytes of data, making fast and reliable analysis essential. Researchers are developing AI tools alongside the detector while working with national laboratories and universities to build the computing infrastructure needed to handle that flood of information.
The collaboration now includes more than 1,500 scientists and engineers from over 35 countries and CERN, while also training the next generation of researchers.
The AI systems will need to be developed and tested alongside the experiment, but if they work as intended, DUNE could become a model for how AI helps scientists operate massive experiments and find rare signals hidden in overwhelming amounts of data.