AI Is Unlocking the Secrets of the Universe

— A CERN Researcher's View on the AI Revolution in Physics

BH | March 2026

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One hundred meters beneath the Swiss-French border near Geneva lies a circular tunnel 27 kilometers in circumference. This is the LHC (Large Hadron Collider) — the largest experimental apparatus ever built by humankind. It accelerates protons to nearly the speed of light, smashes them head-on, and reads the secrets of the universe's fundamental particles from the debris. This is where the Higgs boson was discovered in 2012.

This colossal machine produces 40 million collisions per second. Each collision generates thousands of particles. But only a tiny fraction of these collisions are physically meaningful. Imagine 40 million books pouring out every second, and only one contains the sentence you're looking for.

AI has stepped in to find that sentence. And thanks to AI, analyses that were once unthinkable are gradually becoming possible.

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A Distant Goal, But One Step Closer

In 2025, the CMS collaboration (one of two major detector teams at the LHC) published a remarkable paper. They had captured the moment when a Higgs boson decays into a pair of "charm quarks" with unprecedented precision.

Why does this matter? The Higgs boson is the particle that gives mass to all fundamental particles. Precisely measuring how it decays — into which particles, and how often — is key to understanding why the universe looks the way it does. Among all decay channels, the charm quark decay is especially difficult to measure because the signal from charm quark jets is nearly indistinguishable from background noise.

Fully achieving this measurement remains a distant goal. But AI made the first step possible. A Graph Neural Network (GNN), trained on hundreds of millions of simulated jets, learned to identify the traces of charm quarks. A Transformer network — the same architecture behind ChatGPT, but trained on collision events instead of conversations — classified the events. The result: roughly 35% improvement in precision over previous methods.

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Mixed Feelings

As an active researcher, looking at these results stirs mixed emotions.

On one hand, there's wonder. We can now peer into the workings of the universe — why it looks the way it does, why matter has mass — with speed and precision unimaginable a decade ago. Measurements that seemed like fantasy are now within reach.

On the other hand, a weighty question follows. If AI is this good, do we still need graduate students? Professors? Universities?

This is not an exaggeration. Let me describe what's actually happening in particle physics right now. Every physics analysis has reached a point where it cannot proceed without machine learning. Data selection, signal identification, background rejection, systematic uncertainty estimation — ML is embedded in nearly every step of analysis. Publishing a paper without ML is like filing your taxes without a calculator. You could do it, but nobody does.

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But Scientists Are Conservative

There's something important to note here.

Scientists are far more conservative than you might think.

In particle physics, claiming a new discovery requires exceeding "5 sigma" — meaning the probability of it being a statistical fluke must be less than 1 in 3.5 million. "Probably correct" doesn't cut it. You must prove it's correct.

People raised in this culture don't easily hand everything over to an AI that runs on probabilities. Even when AI says "this is a charm quark," physicists need to understand why it made that judgment. Black boxes are not accepted in physics.

Given that hypothesis testing is the essence of science, it seems the technology still has a long way to go before we can entrust everything to AI.

But one thing is clear. AI is already serving as an exceptionally good tool for advancing science. AI is something like an "extremely fast research assistant" for scientists. It classifies thousands of data points, finds patterns, and narrows down candidates. But deciding whether those results are "physically meaningful" — that's not the assistant's job.

Data measured by physics-based instruments (detectors, accelerators) and judgments made by human-created probabilistic algorithms are fundamentally different in nature. The former directly reflects the laws of nature; the latter is an approximation learned from patterns in data. AI is a tool, but it is an inherently different kind of tool from a physics instrument.

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The Next Chapter for the LHC

In 2025, the LHC completed its final physics season and shut down. It is now undergoing a four-year upgrade to become the HL-LHC (High-Luminosity Large Hadron Collider). When it restarts in 2030, the data flow will be ten times what it is today.

Analyzing that data with humans alone will be impossible. AI will be essential.

But asking "why does this result matter" and "what does this mean for our understanding of the universe" — that remains, for now, a human responsibility.

At least, for now.

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🔗 Sources

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