A new substructure has been found in particle jets, a discovery only possible because hundreds of terabytes of data from the Large Hadron Collider’s CMS experiment were released to the public. This represents several hundred million collisions that can be analyzed by anyone that has the time.
One such person is Professor Jesse Thaler from MIT. With his team, they looked at the features of particle jets, which are produced by high-energy collisions between protons. They discovered that it’s possible to estimate a universal property known as a splitting function, which is often calculated theoretically but had not been measured until now. The results are published in Physical Review Letters, and it represents the first independent study to use the CMS data.
“In our field of particle physics, there isn’t the tradition of making data public,” Thaler said in a statement. “To actually get data publicly with no other restrictions – that’s unprecedented.”
“This is a resource that we now have, which is new in our field,” Thaler added. “I think there was a reluctance to try to dig into it, because it was hard. But our work here shows that we can understand in general how to use this open data, that it has scientific value, and that this can be a stepping stone to future analysis of more exotic possibilities.”
The discovery comes from quantum chromodynamics (QCD), the branch at the very heart of particle physics. QCD describes how quarks behave with gluons, the particles that carry the nuclear force. To predict the pattern that particles will have after a collision, scientists use the splitting function and get the ideal structure of the jet.
“It’s this fractal-like process that describes how jets are formed,” Thaler said. “But when you look at a jet in reality, it’s really messy. How do you go from this messy, chaotic jet you’re seeing to the fundamental governing rule or equation that generated that jet? It’s a universal feature, and yet it has never directly been seen in the jet that’s measured.”
The team applied a novel strategy to find the order in the collisional chaos. The idea, devised by lead authors Andrew Larkoski and Simone Marzani, let the researchers find the splitting function from the data.
The team analyzed over 750,000 collisions from the open-source data, showing that the splitting function can indeed be used to predict real events.
“No one doubts this equation, but we were able to expose it in a new way,” Thaler added. “This is a clean verification that things behave the way you’d expect. And it gives us confidence that we can use this kind of open data for future analyses.”