"From quarks to nuclei: machine learning the structure of matter"
Presented by Phiala Shanahan, MIT
Thursday, June 4, 2020, 12:00 pm — Webcast
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. Bluejeans link: https://bluejeans.com/806818825
Hosted by: Akio Tomiya
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