New York University physicists have created new techniques that deploy machine learning as a means to significantly improve data analysis for the Large Hadron Collider (LHC), the world’s most powerful particle accelerator.

“The methods we developed greatly enhance our discovery potential for new physics at the LHC,” says Kyle Cranmer, a professor of physics and the senior author of the paper, which appears in the journal Physical Review Letters.

Located at the CERN laboratory near Geneva, Switzerland, the LHC is probing a new frontier in high-energy physics and may reveal the origin of mass of fundamental particles, the source of the illusive dark matter that fills the universe, and even extra dimensions of space.

In 2012, data collected by the LHC backed the existence of the Higgs boson, a sub-atomic particle that plays a key role in our understanding of the universe. The following year, Peter Higgs and François Englert received the Nobel Prize in Physics in recognition of their work in developing the theory of what is now known as the Higgs field, which gives elementary particles mass.

NYU researchers, including Cranmer, had searched for evidence of the Higgs boson using data collected by the LHC, developed statistical tools and methodology used to claim the discovery and performed measurements of the new particle establishing that it was indeed the Higgs boson.

The new methods outlined in the Physical Review Letters paper offer the possibility for additional, pioneering discoveries.