Particle Physics meets Machine Learning
Meeting ID: 939 0317 8346
Modern machine learning has had an outsized impat on many scientifc fields, and particle physics is no exception. What is special about particle physics, though, is the vast amount of theoretical and experimental knowelde that we already have about many problems in the field. In this colloquium, I present two case studies involving quantum chromodynamics (QCD), at the Large Hadron Collider (LHC) highlighting the fascinating interplay between theoretical principles and machine learning strategies. First, by catalogining the space of all possible QCD measurements, we (re)discoverd technology relevant for self-dricing cars. Second, by quantifying the similarity between two LHC collisions, we unlocked a class of nanparametric machine learining techniques based on optimal transport. In addition to providing new quantitative insights into QCD, these techniques enable new ways to visualize data from the LHC.
תאריך עדכון אחרון : 05/12/2022