info:eu-repo/semantics/article
Learning the latent structure of collider events
Date
2020-10Registration in:
Dillon, B. M.; Faroughy, D. A.; Kamenik, J. F.; Szewc, Manuel; Learning the latent structure of collider events; Springer; Journal of High Energy Physics; 2020; 206; 10-2020; 1-48
1029-8479
CONICET Digital
CONICET
Author
Dillon, B. M.
Faroughy, D. A.
Kamenik, J. F.
Szewc, Manuel
Abstract
We describe a technique to learn the underlying structure of collider events directly from the data, without having a particular theoretical model in mind. It allows to infer aspects of the theoretical model that may have given rise to this structure, and can be used to cluster or classify the events for analysis purposes. The unsupervised machine-learning technique is based on the probabilistic (Bayesian) generative model of Latent Dirichlet Allocation. We pair the model with an approximate inference algorithm called Variational Inference, which we then use to extract the latent probability distributions describing the learned underlying structure of collider events. We provide a detailed systematic study of the technique using two example scenarios to learn the latent structure of di-jet event samples made up of QCD background events and either tt¯.