Joint TUNL/HEP Seminar: Machine Learning in Nuclear Physics Research

Thursday, March 4, 2021 - 2:00pm

Michelle Kuchera

Machine learning has become ubiquitous in data-rich applications. Fundamental physics research provides an exciting realm for machine learning research with applications ranging from experimental data acquisition through making theoretical predictions. This talk will step through machine learning theory from neural networks to Generative Adversarial Networks, highlighting applications in low through high energy nuclear physics research. Specifically, I will focus on applications that our group, ALPhA (Algorithms for Learning in Physics Applications), have completed in collaboration with the Facility for Rare Isotope Beams and the Thomas Jefferson National Accelerator Facility. I will highlight areas where nuclear physics applications can inform machine learning innovation, providing a rich environment for the advancement of both nuclear physics and machine learning research.