It is thought that shortly after the Big Bang, the early universe was filled with scorching quark-gluon plasma. This then cooled microseconds later to form the building blocks of all the matter found within our universe. But while cooling, a fraction of these quarks and gluons collided randomly to form short-lived X particles.
These X particles are very rare. Scientists think that they may be created in particle accelerators through quark coalescence.
MIT physicists have detected X particles in the quark-gluon plasma produced in the Large Hadron Collider (LHC) at CERN. Using machine-learning techniques, physicists analyzed more than 13 billion heavy-ion collisions.
Each of these collisions had generated tens of thousands of charged particles. In this ultradense, high-energy particle soup, the team found 100 X particles of a type known as X (3872), named for the particle’s estimated mass.
This is the first time scientists have detected X particles in a quark-gluon plasma.
Lead author Yen-Jie Lee, the Class of 1958 Career Development Associate Professor of Physics at MIT, said, “This is just the start of the story. We’ve shown we can find a signal. In the next few years, we want to use the quark-gluon plasma to probe the X particle’s internal structure, which could change our view of what kind of material the universe should produce.”
The X(3872) was first seen in 2003 by the Belle experiment and then quickly confirmed by BaBar, CDF, and D0. It was discovered in a particle collider in Japan. These rare particles decay too quickly that scientist could not study their structure in detail. However, it was theorized that X (3872) and other exotic particles might be better illuminated in a quark-gluon plasma.
Lee said, “Theoretically speaking, there are so many quarks and gluons in the plasma that the production of X particles should be enhanced. But people thought it would be too difficult to search for them because there are so many other particles produced in this quark soup.”
The machine learning algorithm scientists used in this study were trained to pick out decay patterns characteristic of X particles.
After forming particles in quark-gluon plasma, they quickly break down into “daughter” particles that scatter away. This decaying pattern for X particles is different than other particles.
Scientists then identified the key variables that describe the shape of the X particle decay pattern. They trained a machine-learning algorithm to recognize these variables then fed the actual algorithm data from the LHC’s collision experiments.
Their algorithm successfully picked out the key variables likely to result from decaying X particles.
Lee said, “It’s almost unthinkable that we can tease out these 100 particles from this huge dataset.”
MIT postdoc Jing Wang said, “Every night, I would ask myself, is this a signal or not? And in the end, the data said yes!”
Scientists are further planning to collect more data to demonstrate the X particle’s structure.
- A. M. Sirunyan et al. Evidence for X(3872) in Pb-Pb Collisions and Studies of its Prompt Production at √sNN= 5.02 TeV. DOI: 10.1103/PhysRevLett.128.032001