סמינר בחלקיקים: From quarks to hadrons and nuclei: machine learning for lattice field theory

Zoom: https://us02web.zoom.us/j/83283854432?pwd=VXdmZ1lTbkl4SEZnVndqcUZNQWg0UT09&from=msft

Abstract:

With advances in supercomputing, we are beginning to quantitatively understand hadron and 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 achieve full systematic control. I will describe the physics challenges, and outline how new machine learning tools have the potential to provide a revolutionary way to enable currently-intractable calculations to reveal the physics of nuclei from the Standard Model.

Special messages:

1. Note the unusual time as the speaker is in the states.

2. Dr. Phiala Shanahan  is the recipient of the 2020 Kenneth G. Wilson Award for Excellence in Lattice Gauge Theory is  

   "For excellence in the study of hadrons and nuclei in lattice QCD and for pioneering the application of machine learning and artificial intelligence techniques to lattice field theory".

מארגן הסמינר: פרופ' ארז עציון וד"ר לירון ברק