New Methods to Boost Data Analysis For Large Hadron Collider

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For particle physicists eager to explore new frontiers, spotting the Higgs boson has become a bittersweet triumph. Detected in 2012 at the world’s biggest atom smasher, the Large Hadron Collider (LHC), the long-sought particle filled the last gap in the standard model of fundamental particles and forces.

Scientists say they have created new techniques that deploy machine learning to significantly improve data analysis for the Large Hadron Collider (LHC), the world’s most powerful particle accelerator.

Now, the Higgs itself may offer a way out of the impasse. Experimenters at the LHC, located at CERN, the European particle physics laboratory near Geneva, Switzerland, plan to hunt for collisions that produce not just one Higgs boson, but two. Finding more of these rare double-Higgs events than expected could point to particles or forces beyond the standard model and might even help explain the imbalance of matter and antimatter in the universe.

The Higgs boson plays a special role in the standard model, which describes how a dozen types of particles interact through three forces: electromagnetism and the weak and strong nuclear forces. (The theory does not include gravity, a major failing.) The forces in the model arise from certain mathematical symmetries. But that math works only so long as the particles do not start out with mass. So mass must somehow emerge through interactions among the otherwise mass-less particles themselves.

Double-Higgs events promise a way to tell for sure, by revealing how strongly the Higgs field interacts with itself. An electric field vanishes if there’s no charge around, but the Higgs field must always linger in the vacuum – or else it wouldn’t be able to impart mass to other particles. The standard model presumes this happens with a Higgs field that interacts with itself and minimizes its energy not by vanishing, but by taking a nonzero strength.

The study, published in the journal Physical Review Letters, offers the possibility for additional, pioneering discoveries. Professor Kyle Cranmer said, “In many areas of science, simulations provide the best descriptions of a complicated phenomenon, but they are difficult to use in the context of data analysis” and further added, “The techniques we have developed build a bridge allowing us to exploit these very accurate simulations in the context of data analysis.”