CERN Physicists Use AI To Understand the ‘God Particle’

Key Takeaways

• Physicists at the European Organization for Nuclear Research (CERN) use AI to search for subatomic particle evidence.
• CERN researchers hope advances in machine learning will help them learn more about the Higgs boson.
• A major goal of the project is to measure the coupling strength of two Higgs bosons.

Ever since it was first hypothesized in the 1960s, the Higgs boson, also known as the “god particle,” has been one of the most elusive objects in particle physics.

But although experiments at the CERN Large Hadron Collider (LHC) finally provided evidence of the Higgs boson’s existence in 2012, the task of unlocking its secrets remains unfinished.

Now, as researchers scour reams of LHC data in a bid to decode the fundamental structure of matter, artificial intelligence is helping them uncover clues about the mysterious particle’s behavior.

What Is a Higgs boson?

The Higgs boson is a central component of what scientists call the Standard Model, a widely accepted framework that accurately predicts the behavior of almost all known particles and forces aside from gravity.

The term god particle was coined by the experimental physicist Leon Max Lederman, who believed unlocking the Higgs boson’s secrets was critical to understanding the origins of the universe.

So crucial is the Higgs boson to the Standard Model that the European Organization for Nuclear Research (CERN) spent decades and billions of dollars trying to prove its existence and verify the framework’s theoretical assumptions.

Having validated the Standard Model’s basic predictions, CERN researchers have since turned their attention to other questions. But the Higgs boson remains a crucial puzzle piece that is fiendishly difficult to observe.

How AI Helps Physicists Hunt for New Particles

To identify signs of particles like the Higgs boson, CERN researchers work with mountains of data generated by LHC collisions.

Hunting for evidence of an object whose behavior is predicted by existing theories is one thing. But having successfully observed the elusive boson, identifying new and unexpected particles and interactions is an entirely different matter.

To speed up their analysis, physicists feed data from the billions of collisions that occur in LHC experiments into machine learning algorithms. These models are then trained to identify anomalous patterns.

For example, in 2024, scientists working in CERN’s ATLAS and CMS collaborations used data collected from sprays of particles known as jets to identify “atypical jet signatures” that may lead to new experimental breakthroughs.

The Search for Higgs Self-Coupling

As well as searching for previously undetected particles, a major goal for CERN is to observe two Higgs bosons at the same time.

Such an observation is critical for physicists’ understanding of a phenomenon known as Higgs self-coupling, i.e. the interaction of the Higgs boson with itself.

Because the Standard Model predicts a specific “coupling strength” for Higgs self-coupling, any deviation from this prediction would indicate new physics beyond the Standard Model.

In turn, the accurate measurement of the phenomenon could help prove or disprove competing theories such as supersymmetry, extra dimensions, or composite Higgs models.

The problem, however, is that a proton collision is 1000 times less likely to produce two Higgs bosons than they are one.

To overcome this challenge, scientists are working on more powerful AI tools to help sift through collision data.

In a recent interview with the Guardian, CERN’s next director general, Mark Thomson, said these novel machine learning techniques are leading to “very big improvements” in the analysis of LHC data that could soon generate an accurate measurement of Higgs self-coupling strength.

Such a measurement would be CERN’s most important breakthrough since 2012, with profound implications for particle physics and helping to explain how matter itself comes into existence.