Machine Learning Knowledge Transfer - From LHC to EIC by Sanmay Ganguly

Explore machine learning applications in particle physics through this 41-minute lecture on knowledge transfer from the Large Hadron Collider (LHC) to the Electron-Ion Collider (EIC). Delve into how techniques developed for LHC experiments can be adapted for the upcoming EIC, which will probe proton and neutron structure at unprecedented levels. Learn about the potential of machine learning to address key questions in nuclear physics, including quark and gluon distributions, nucleon mass composition, and spin structure. Gain insights into the challenges and opportunities of applying advanced data analysis methods across different experimental setups in high-energy physics.