University of LiègeULgFaculty of EngineeringFacSALibrary News   
Seminar : What does the Revolution in Artificial Intelligence Mean for Particle Physics?


Particle physics aims to answer profound questions about the fundamental
building blocks of the Universe through enormous data sets collected at
experiments like the Large Hadron Collider at CERN. Inference in this context
involves two extremes. On one hand the theories of fundamental particle
interactions are described by quantum field theory, which is elegant, highly
constrained, and highly predictive. On the other hand, the observations come
from interactions with complex sensor arrays with uncertain response, which
lead to intractable likelihoods. Machine learning techniques with
high-capacity models offer a promising set of tools for coping with the
complexity of the data; however, we ultimately want to perform inference in
the language of quantum field theory. I will discuss likelihood-free
inference, generative models, adversarial training, and other recent progress
in machine learning from this point of view.