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To better understand the universe, it is crucial to gain more knowledge about the elementary particles such as electrons and quarks - and how they interact. Using Artificial Intelligence, Dr. Seth Moortgat, a scientist at the Interuniversity Institute for High Energies of the VUB and ULB, developed two innovative methods that were used at CERN. The first method makes it easier to identify different types of these quarks. The second method increases the sensitivity of the data analysis when comparing the results, allowing the theoretical models to be verified faster and possibly excluded. Because of this, science is one step closer to tracing still unknown natural phenomena. Moortgat was nominated for the prestigious Lindau Nobel Laureate meeting at the beginning of July, in which 39 Nobel Prize winners in physics will participate this year.
CERN, the European research laboratory for elementary particle physics, is home to the most powerful particle accelerator on earth, the Large Hadron Collider (LHC), in which protons are accelerated to record energies and collided with other protons. With these collisions, conditions are simulated as they were within a fraction of a second after the Big Bang. This is where the search for yet unknown physics phenomena begins. Seth Moortgat contributes to this in the search for mysteries such as dark matter:
“The Standard Model of particle physics describes the basic building blocks of the universe and the forces responsible for the interactions between these particles. The discovery of the Brout-Englert-Higgs particle in 2012 was the final milestone to complete this model. However, the model remains incomplete as it fails to describe phenomena such as dark matter, the mass of neutrinos, or even gravity”, Dr. Seth Moortgat explains.
Moortgat conducts fundamental research into phenomena that could be hidden behind the interactions between the three heaviest matter particles: charm quarks, bottom quarks and top quarks. With Machine Learning (the principle behind artificial intelligence: self-learning machines that can be trained to recognize specific patterns in large amounts of data) he developed innovative algorithms to identify the different types of quarks in the detector and to distinguish between them.
“With the development of innovative Machine Learning methods, we can now measure how often the three heaviest quarks are produced together in the billions of particle collisions that occur every second in the LHC.” With these observations, Moortgat started using AI to exclude a large number of new physics models from the measurement results. “Excluding models is extremely important in order to understand which unknown phenomena we apparently still overlook today.”
The results of his research appeared in the renowned Journal for High Energy Physics. Moortgat was selected to participate in the 69th hosting of the prestigious international Lindau Nobel Laureate meetings. At the beginning of July 2019, he will be given the opportunity to participate, together with a select group of 600 young scientists from all over the world, in open discussions with around 40 Nobel Prize winners in physics.
Doctoral thesis of Seth Moortgat: https://cds.cern.ch/record/2676133
Publication: Journal for High Enegery Physics:https://link.springer.com/article/10.1007/JHEP11(2018)131
CMS experiment CERN: https://home.cern/science/experiments/cms
69th Lindau Nobel Laureate meeting: https://www.mediatheque.lindau-nobel.org/meetings/2019