Session 2WC: Computational Advances in Nuclear Science II

Machine learning in nuclear physics experiments

While machine learning methods have been applied to data analysis in experimental physics since the 1990s, the development of flexible, advanced computational tools have allowed for vast innovation in recent years. This talk will discuss the current status of machine learning applications in experimental nuclear physics, highlighting examples of recent work at various nuclear physics facilities. Visions of the future of Artificial Intelligence technology in nuclear physics will also be presented, drawing from work done by the community in preparation for the AI for Nuclear Physics whitepaper.   

Pushing the limits of no-core shell model calculations

The no-core shell model was one of several innovations that drove a renaissance in rigorous nuclear structure calculations. Nonetheless, it hits the computational wall at the end of the p-shell. I discuss some strategies to extend the computational reach of no-core shell model calculations to heavier nuclei.   

Quantum Monte Carlo calculations of lepton-nucleus interactions

In this talk, I will report on recent progress in microscopic calculations of lepton-nucleus interactions and their impact on ongoing experimental efforts in Nuclear Physics, Fundamental Symmetries and Neutrino Physics. In particular, I will present calculations of beta decay and neutrinoless double beta decay matrix elements in light nuclei. In addition, I will discuss a novel computational method developed to study scattering of electrons and neutrinos from nuclei and their relevance to long-base neutrino oscillation experiments and JLab experimental programs.