  Individual events at high-energy colliders like the LHC can be represented by
a sequence of measurements, or 'point patterns' in an observable space.
Starting from this data representation, we build a simple Bayesian
probabilistic model for event measurements useful for unsupervised event
classification in beyond the standard model (BSM) studies. In order to arrive
to this model we assume that the event measurements are exchangeable (and apply
De Finetti's representation theorem), the data is discrete, and measurements
are generated from multiple 'latent' distributions, called 'themes'. The
resulting probabilistic model for collider events is a mixed-membership model
known as Latent Dirichlet Allocation (LDA), a model extensively used in natural
language processing applications. By training on point patterns in the primary
Lund plane, we demonstrate that a two-theme LDA model can learn to distinguish
in unlabelled dijet events the hidden new physics patterns produced by a BSM
signature from a much larger QCD background. Based on 1904.04200 and
2005.12319.
