From quarks to nuclei: machine learning the structure of matter

10/04/2019 - 3:30pm to 4:30pm
Speaker(s) / Presenter(s): 
Phiala Shanahan, MIT
Type of Event (for grouping events):
I will discuss the status and future of lattice Quantum Chromodynamics (QCD) calculations for nuclear physics. With advances in supercomputing, we are beginning to quantitatively understand nuclear structure and interactions directly from the fundamental quark and gluon degrees of freedom of the Standard Model. Recent studies provide insight into the neutrino-nucleus interactions relevant to long-baseline neutrino experiments, double beta decay, and nuclear sigma terms needed for theory predictions of dark matter cross-sections at underground detectors. The rapid progress in this field  has been possible because of new algorithms but challenges still remain to reach the large nuclei used in many of these experiments.  Recently, machine learning tools have been shown to provide a potentially revolutionary way to address these challenges and allow a Standard Model understanding of the physics of nuclei.
Host: Keh-Fei Liu


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