UNDERSTANDING THE QUARK GLUON PLASMA WITH MACHINE LEARNING
What can modern data science tell us about jets in high energy nuclear collisions?
High energy collisions of nuclei can be used to create tiny droplets of quark gluon plasma, the primordial matter of the universe. High energy quarks and gluons that are created alongside the plasma develop into QCD jets which can be detected. It is hoped that by understanding the interaction of these jets with quark gluon plasma we can understand important properties of the plasma.
Using QCD jets to investigate quark gluon plasma has been one of the priorities of the US Nuclear Physics program with large investments into the experimental programs at Brookhaven National Laboratory and at CERN. Studies of quark gluon plasma will have a profound impact on our understanding of strongly interacting matter and of the early Universe.
The creation of jets happens on time scales of less than 10-20 seconds. Therefore particle detectors can only record information about the very last particles formed in a jet. In the first phase of this project we study if the history of the time evolution of a jet can be reconstructed from these final particles using machine learning. Specifically we look to reconstruct some properties of early radiation emitted from the original quark or gluon, including the angle at which the radiation is emitted and its share of the energy. This is done by training machine learning algorithms using computer simulations of jets before then applying those algorithms to experimental data.
For more information, please contact Prof. Rainer J. Fries at udnulle nullTOD nullumanulltnull TnullOD nullpmoc nullTAnull nullseinullrFnullJRnull.