At CERN, AI will drive future discoveries

What does the search for the God particle at CERN look like? CGI imagery came up with the above. Copyright (C) 2026 Shutterstock Editorial. No Use Without Permission.

Every second, CERN's Large Hadron Collider (LHC) produces 40 million particle collisions – far more data than any computer on Earth could ever store or analyse. So CERN scientists are letting AI make split-second decisions, in real time, about which of those collisions might contain the next big discovery. It’s one of many ways in which AI could transform particle physics work.

As CERN plans a new, far more expensive collider to replace the LHC in the 2040s, physicists say AI won’t just crunch numbers after the fact– it will help design the machine itself, choose its materials, and decide what questions it’s even built to ask.

When scientists at the CERN particle physics laboratory discovered the Higgs Boson in 2012, it was a revolution in our understanding of the universe. The finding came after four decades of searching. And it wouldn’t have been possible without machine learning algorithms, which were “something like the great-grandfather of what you now call AI,” says Maurizio Pierini, a physicist at CERN. 

Today, the descendants of those algorithms are making inroads in all areas of particle physics. “We are going to use AI more and more,” former CERN director Fabiola Gianotti told Swissinfo. Physicists, like scientists in other fields, are using AI before an experiment, to prepare it, and after an experiment, to analyse data. But at CERN, they are also pushing the technology into unexplored directions. “What defines our specificity is that we also are deploying algorithms in the middle of the experiment, as part of the actual data taking,” Pierini says.

The future of CERN: a series

The stories in this series look at where the world’s largest particle physics laboratory stands in its scientific ambitions and its efforts to remain an international crossroads for understanding our universe. 

CERN’s new AI-based tools arrive as the Geneva-based institution is upgrading much of its physical equipment. This year, work will begin updating the LHC to a machine with higher collision rates, which means more data to analyse. Next, the lab must complete the design and obtain approval for a collider, called the Future Circular Collider (FCC), that will replace the LHC in the 2040s. CERN physicists interviewed for this story agree that AI could be instrumental not just in research and analysis, but in helping to design the new collider, keeping costs down, and attracting brilliant minds back to particle physics.

 “Thanks to AI, everything will be done differently: better, faster, more technologically advanced, and AI will help us probe the open questions in particle physics,” says Maria Spiropulu, a particle physicist at the California Institute of Technology and a CERN collaborator.

AI for the Higgs Boson

The first use of machine learning at CERN was in 1987, when scientists developed a system to find faults in a machine called the Proton Synchrotron.

Later on, CERN scientists used another AI ancestor to make use of the LHC. In the LHC, particles collide at energies of up to 13 trillion electron volts (TeV) and produce forty million collisions every second. Each collision results in traces that are picked up by detectors, giant machines that surround the LHC collision zones. The stream of data is so huge and fast that “no computing infrastructure on this planet can handle it,” Pierini says. “You need to filter the data, and you need to have an algorithm that decides what’s interesting and what’s not.”

That’s exactly what happened when CERN was hunting for the Higgs Boson. The so-called God Particle gives mass to other particles, but it’s rarely produced during a collision, and it exists only for a fleeting instant. Scientists, however, knew what they were looking for. Peter Higgs predicted the existence of the boson in the 1960s based on existing understandings of particle physics, and finding it was a matter of filtering the data to find evidence.

To do this, researchers loaded machine learning algorithms onto the LHC’s hardware and programmed it to look for traces that would be consistent with Peter Higgs’s calculations. From the streams of data, the machine selected the most likely instances where a Higgs boson was produced. In the end, the algorithms could filter 1,000 signals per second, allowing for the first clear observations of the God Particle. “That’s why we can say that AI helped the Higgs boson discovery,” Pierini says.

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AI for the unknown

Despite that success, Pierini was not content with the technology’s performance. His dream was to make further use of the chips that make up the filter by installing quicker and more powerful algorithms. The challenge lies in the fact that the LHC detector hardware is so limited that “you cannot put ChatGPT in it,” Pierini says. Instead, the scientist opted to use neural networks, which are powerful computational models that can fit onto tiny hardware, yet still execute complex functions quickly. That way, CERN scientists could make algorithms do their job in nanoseconds. “These results opened a completely new avenue for us”, says Pierini.

Now, using the neural networks on the same hardware, scientists can run multiple algorithms to look at all data in real time. Pierini is interested in using this advancement to find collisions that deviate from patterns predicted by existing theories. The approach, called anomaly detection, is similar to what banks use to identify fraudulent credit card charges. Applied to LHC data, this could identify new, anomalous events that scientists didn’t yet know they should be looking for. “This is a way to discover something unexpected,” Pierini says.

The Italian scientist hints at the fact that particle physicists have long been focused on confirming or disproving theories that were developed decades ago. Pierini’s approach, assisted by AI, may help particle physics go back to the essence of the scientific method, which starts with observing nature and asking questions to develop new theories and build new understandings.

“AI can certainly make our under-the-lamp-post search better, but I’m more interested in the AI algorithm looking behind my back,” Pierini says. The new technology, nicknamed trigger AI, has been tested in the LHC and will be implemented in the updated machine and in future colliders.

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AI for future collidors

As AI algorithms advance, scientists can use them to analyse data more precisely after an experiment, sometimes increasing precision hundreds of times beyond what’s currently feasible and saving millions of Swiss francs in the process, according to Pierini

This advance could become pivotal to finding hints of rare and complicated events among millions of similar traces. Producing two Higgs bosons at the same time is an example of an extremely rare event. A double Higgs boson would give insights into how the Higgs field gives masses to particles, “a big unknown in high-energy physics,” adds Pierini. Efficient data analysis will become even more important with the upgraded LHC, the so-called High-Luminosity LHC, which will deliver five to six times as much data as the current collider.

Artificial intelligence will also have a role in building new particle colliders, like the FCC, which is slated to replace the LHC. “AI will be instrumental for everything from the design of the detectors to running the experiments to doing the monitoring systems,” says Spiropulu, the CERN collaborator who works at the California Institute of Technology.

For example, AI could help develop new, less expensive materials for the superconducting magnets that are essential for a collider’s function. AI tools could also influence the detectors’ design. While particle physicists are currently relying on their experience to design the next generation of detectors, in the future, “scientists will ask AI to fully design detectors, which are optimised for the physics and the tasks requested,” says Pierini.

But a collider will still be required to produce the data, as “AI won’t allow us to make the same experiments of the FCC without the FCC itself,” Pierini says.

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A shrinking field

Pierini imagines that if AI is used to handle more of the tedious tasks in particle physics work, it will make the job seem more exciting and interesting to new talent. The new technology might also lead to new, attractive jobs that involve cutting-edge AI applications. But if the field continues to shrink, AI will assist die-hard particle physicists in taking on new, challenging experiments, since “every researcher will be enhanced by AI agents (or tools),” Pierini explains. “AI is going to  keep the field alive, one way or another.”

Edited by Veronica De Vore/ds