Matteo Cremonesi Programs New Approaches for High Energy Physics
Assistant Professor of Physics Matteo Cremonesi is developing software to help power discovery of high energy physics.
A new member of Carnegie Mellon University's Department of Physics, Cremonesi joins some of his collaborators working on the Compact Muon Solenoid (CMS) experiment at CERN's Large Hadron Collider (LHC).
"Carnegie Mellon has a very strong group working on the CMS," Cremonesi said. The group includes Physics Professor Manfred Paulini, and John Alison and Valentina Dutta, assistant professors of physics.
Housed at the LHC, the CMS collaboration uses a five-story detector that searches for and provides information about particles not predicted by the standard model of particle physics , e.g., the properties of the Higgs boson.
Cremonesi started working with the CMS experiment in 2015 while he was a postdoctoral researcher at Fermi National Accelerator Laboratory. He specialized in computing operations, software, and research and development. For much of his work he evaluated existing data science tools used by companies such as Facebook and Google to find new applications to physics.
"I managed a team that tried to popularize the utilization of industry standard tools for analysis, which could speed up the way we do our research" he said.
More recently Cremonesi has been looking at applying artificial intelligence and machine learning to high energy physics data. Using these tools for analysis as experiments occur could be key to the CMS experiment, he said.
"A very complex machine learning algorithm in real time could allow for very fast selection of data that may look interesting and improve the selection of where focus could be spent," he said.
Cremonesi recently started a new effort to upgrade the CMS online event filter processing system, called the "trigger," which is composed of a combination of algorithms that save only the information on collisions that show characteristics of interest as they occur.
Cremonesi is determining whether machine learning algorithms in the trigger can enhance the potential of the CMS experiment to discover dark matter.
"The local CMS team is trying to study the applicability of machine learning at different steps of our research, leveraging the strengths of CMU in computer science and artificial intelligence," said Cremonesi, who is the CMS experiment delegate to the LHC Dark Matter Working Group, a cross-experiment effort that defines a set of shared guidelines for physicists searching for dark matter at the LHC. "The effort on the trigger is still new, but it can open up new opportunities to finally discover dark matter with the LHC."
Along with his research, Cremonesi taught undergraduate courses in experimental physics in the spring of 2023 and introduction to nuclear and particle physics in the fall of 2023. He said he hopes more undergraduate students will consider high energy physics research earlier in their careers.
"I would like to see if providing better computing tools can help undergraduates take a larger role at that stage of their career," he said. "I'd like to see what we can do with their enthusiasm and a vision for the future of the field."
Cremonesi earned his bachelor's and master's degrees from the University of Rome II and his doctorate from the University of Oxford.