Experience with Deep Learning in Particle Physics
journal contributionposted on 13.03.2019 by Kyle Cranmer
Any type of content formally published in an academic journal, usually following a peer-review process.
Particle physics is a field equipped with a high-fidelity simulation that spans a hierarchy of scales ranging from the quantum mechanical interaction of fundamental particles to the electronic response of enormous particle detectors. These simulators provide a causal, generative model for the data. Moreover, they are are stochastic and non-differentiable. Most inference problems in particle physics can be framed as an inverse problem where the simulation represents the forward model. I will describe recent experiences and lessons learned from attacking these problems using deep learning and present examples of incorporating domain knowledge into the models.