AI Surpasses CERN Physicists in Analyzing the Large Hadron Colliders Data with Quantum Interference Breakthrough

A neural network known as Neural Simulation-Based Inference (NSBI) has effectively tackled the challenge of quantum interference, delivering results that scientists anticipated could only be achieved through traditional methods within the next 15 years.

The story began in 2017 when the head of the ATLAS collaboration tasked graduate student Aishik Ghosh with developing one of the detection methods for the Higgs boson—a particle that imparts mass to other elementary particles. The Higgs boson was first identified at the Large Hadron Collider (LHC) in 2012 and is produced during high-energy proton collisions.

The fundamental complexity of the task arose from the multivariate nature of the decay process. During proton collisions, W bosons can form, and their fusion may create a Higgs boson.

In turn, the Higgs boson decays into Z bosons, which subsequently transform into leptons, including electron-positron pairs. A critical aspect of this process is that the Higgs boson may not appear in the decay chain, creating a methodological challenge—analyzing the absence of a signal rather than its presence.

A major barrier for traditional analysis was the **quantum interference effect.** This phenomenon is similar to wave interference in water but is significantly more complex due to the multidimensional nature of quantum processes. In such scenarios, examining individual particle decay pathways did not provide an accurate representation of events.

The solution was found in employing the NSBI neural network, which had not been previously used for this type of analysis. Unlike conventional methods that focus on separate decay channels, the neural network modeled the entire array of processes while considering their interdependencies. This holistic approach greatly enhanced the accuracy of the analysis.

By December 2024, the ATLAS research group published two scientific papers: the first detailing the methodology and the second presenting the results of a reanalysis of archived data using the neural network approach. The characteristics of the Higgs boson obtained from this analysis were more precise than those established by traditional methods.

“One of the amusing aspects of this method that Aishik pushed so hard is that each time we make a prediction—estimating how well we will perform in 15 years—we completely shatter those predictions,” researchers noted. “So now we have to revise our set of forecasts because we have already reached our previous projections set for 15 years down the line.”

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